From 86376f076c875588e33eeccf2876045884695210 Mon Sep 17 00:00:00 2001
From: Carlos Loucera <carlos.loucera@juntadeandalucia.es>
Date: Tue, 11 Jul 2023 10:21:48 +0200
Subject: [PATCH 01/37] Add pip badge.

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 4ac4331..5afa4a1 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-[![DOI](https://zenodo.org/badge/362395439.svg)](https://zenodo.org/badge/latestdoi/362395439)
+[![DOI](https://zenodo.org/badge/362395439.svg)](https://zenodo.org/badge/latestdoi/362395439) [![PyPI version](https://badge.fury.io/py/drexml.svg)](https://badge.fury.io/py/drexml)
 
 # Drug REpurposing using eXplainable Machine Learning and Mechanistic Models of signal transduction
 

From 8d649f9ff1ff601f1d713c708edbedfda13d3c70 Mon Sep 17 00:00:00 2001
From: Carlos Loucera <carlos.loucera@juntadeandalucia.es>
Date: Tue, 11 Jul 2023 16:54:33 +0200
Subject: [PATCH 02/37] Add pdm badge.

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 5afa4a1..af10557 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-[![DOI](https://zenodo.org/badge/362395439.svg)](https://zenodo.org/badge/latestdoi/362395439) [![PyPI version](https://badge.fury.io/py/drexml.svg)](https://badge.fury.io/py/drexml)
+[![DOI](https://zenodo.org/badge/362395439.svg)](https://zenodo.org/badge/latestdoi/362395439) [![PyPI version](https://badge.fury.io/py/drexml.svg)](https://badge.fury.io/py/drexml) [![pdm-managed](https://img.shields.io/badge/pdm-managed-blueviolet)](https://pdm.fming.dev)
 
 # Drug REpurposing using eXplainable Machine Learning and Mechanistic Models of signal transduction
 

From 987b7eb1f39c148f358b098db1bd7c65ac347511 Mon Sep 17 00:00:00 2001
From: marinaEM <marina.estebanm@gmail.com>
Date: Fri, 28 Jul 2023 00:12:49 +0200
Subject: [PATCH 03/37] Addition of SHAP analysis .py scripts along with two
 drug annotation files on folder resources

---
 drexml/boxplots_shap_scores.py                |   49 +
 drexml/heatmap_plot.ipynb                     | 1314 ++++++++
 drexml/heatmap_shap_scores.py                 |   64 +
 drexml/heatmap_top10KDT_drugs.py              |   64 +
 drexml/histograms_shap_scores.py              |   43 +
 .../drugbank-v050110_alldrugbyaction.tsv      | 2771 +++++++++++++++++
 .../drugbank-v050110_mostdrugeffects_KDTs.tsv |  744 +++++
 drexml/shap_scores_filtering.py               |   42 +
 8 files changed, 5091 insertions(+)
 create mode 100644 drexml/boxplots_shap_scores.py
 create mode 100644 drexml/heatmap_plot.ipynb
 create mode 100644 drexml/heatmap_shap_scores.py
 create mode 100644 drexml/heatmap_top10KDT_drugs.py
 create mode 100644 drexml/histograms_shap_scores.py
 create mode 100644 drexml/resources/drugbank-v050110_alldrugbyaction.tsv
 create mode 100644 drexml/resources/drugbank-v050110_mostdrugeffects_KDTs.tsv
 create mode 100644 drexml/shap_scores_filtering.py

diff --git a/drexml/boxplots_shap_scores.py b/drexml/boxplots_shap_scores.py
new file mode 100644
index 0000000..006490c
--- /dev/null
+++ b/drexml/boxplots_shap_scores.py
@@ -0,0 +1,49 @@
+# -*- coding: utf-8 -*-
+"""
+SHAP scores analysis on boxplots for DREXML.
+"""
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+def create_boxplots(data_folder, filtered_file_name):
+    """
+    This function reads the filtered data, creates boxplots for all score columns, and saves the boxplots to a .pdf file.
+
+    Args:
+    data_folder (str): The path to the data folder.
+    filtered_file_name (str): The name of the shap scores filtered file.
+
+    Returns:
+    The boxplots are saved as a .pdf file.
+    """
+    import pandas as pd
+    import matplotlib.pyplot as plt
+    import seaborn as sns
+
+    # Load the previous filtered table
+    data = pd.read_csv(f"{data_folder}/{filtered_file_name}", sep='\t', index_col=0)
+
+    # Melt the DataFrame to make it suitable for boxplots
+    data.reset_index(inplace=True) ## Reset the index back to a column
+    melted_data = data.melt(id_vars = "circuit_name")
+
+    # Compute median of each column
+    medians = melted_data.groupby("variable")["value"].median().sort_values(ascending=False)
+
+    # Order the data based on medians
+    melted_data["variable"] = pd.Categorical(melted_data["variable"], categories=medians.index, ordered=True)
+
+    # Create the boxplots
+    plt.rcParams['font.family'] = 'DejaVu Sans'
+    plt.figure(figsize=(20, 12), facecolor='white')
+    sns.set_style("whitegrid")  # set Seaborn style to white grid
+    sns.boxplot(x="variable", y="value", data = melted_data)
+    plt.xticks(rotation=40, ha = "right")
+    plt.title("Boxplots of SHAP-Scores")
+    plt.ylabel('SHAP Scores')
+    plt.xlabel('Drug-target')
+    plt.tight_layout()
+    plt.savefig(f"{data_folder}/relevant_drugTargetscores_boxplots.pdf", dpi=300)
+    plt.show()
\ No newline at end of file
diff --git a/drexml/heatmap_plot.ipynb b/drexml/heatmap_plot.ipynb
new file mode 100644
index 0000000..c362dec
--- /dev/null
+++ b/drexml/heatmap_plot.ipynb
@@ -0,0 +1,1314 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "id": "12763c3b-3a9a-4340-9fc5-f3e858c26da6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%reset -f\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "data_folder= \"/home/m3m/INFO_PROYECTO/drexml_paper_paquete/results/Fanconi_Anemia/results\"\n",
+    "\n",
+    "# Read the .tsv files into a DataFrames\n",
+    "shap_values = pd.read_csv(f\"{data_folder}/shap_summary_symbol.tsv\", sep='\\t', index_col=0)\n",
+    "shap_selection = pd.read_csv(f\"{data_folder}/shap_selection_symbol.tsv\", sep='\\t', index_col=0)\n",
+    "stability = pd.read_csv(f\"{data_folder}/stability_results_symbol.tsv\", sep='\\t', index_col=0) \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "id": "8a370d44-b8af-4d65-bfdd-5ff39856adb3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ABAT</th>\n",
+       "      <th>ABCA1</th>\n",
+       "      <th>ABL1</th>\n",
+       "      <th>ACAT1</th>\n",
+       "      <th>ACHE</th>\n",
+       "      <th>ACLY</th>\n",
+       "      <th>ACP3</th>\n",
+       "      <th>ACTN1</th>\n",
+       "      <th>ADA</th>\n",
+       "      <th>ADH1A</th>\n",
+       "      <th>...</th>\n",
+       "      <th>TPH2</th>\n",
+       "      <th>SYT2</th>\n",
+       "      <th>TAAR1</th>\n",
+       "      <th>VKORC1L1</th>\n",
+       "      <th>TRPV3</th>\n",
+       "      <th>TUBB</th>\n",
+       "      <th>PCSK9</th>\n",
+       "      <th>HCAR2</th>\n",
+       "      <th>FFAR4</th>\n",
+       "      <th>CKMT1A</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>circuit_name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Fanconi anemia pathway: RAD51</th>\n",
+       "      <td>-8.689657e-08</td>\n",
+       "      <td>-0.386536</td>\n",
+       "      <td>-0.401960</td>\n",
+       "      <td>-0.396785</td>\n",
+       "      <td>-0.521874</td>\n",
+       "      <td>-0.378718</td>\n",
+       "      <td>-0.300026</td>\n",
+       "      <td>-0.379242</td>\n",
+       "      <td>-0.389304</td>\n",
+       "      <td>-0.389326</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.388704</td>\n",
+       "      <td>-0.389426</td>\n",
+       "      <td>-0.388067</td>\n",
+       "      <td>-0.384761</td>\n",
+       "      <td>-0.384756</td>\n",
+       "      <td>-0.357535</td>\n",
+       "      <td>-0.372284</td>\n",
+       "      <td>-0.359548</td>\n",
+       "      <td>-0.365995</td>\n",
+       "      <td>-0.403467</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fanconi anemia pathway: MLH1 PMS2</th>\n",
+       "      <td>-8.077751e-08</td>\n",
+       "      <td>-0.238399</td>\n",
+       "      <td>-0.269825</td>\n",
+       "      <td>-0.260549</td>\n",
+       "      <td>-0.431095</td>\n",
+       "      <td>-0.230148</td>\n",
+       "      <td>-0.084805</td>\n",
+       "      <td>-0.233656</td>\n",
+       "      <td>-0.252828</td>\n",
+       "      <td>-0.241303</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.242601</td>\n",
+       "      <td>-0.241099</td>\n",
+       "      <td>-0.243983</td>\n",
+       "      <td>-0.235437</td>\n",
+       "      <td>-0.234357</td>\n",
+       "      <td>-0.267757</td>\n",
+       "      <td>-0.214188</td>\n",
+       "      <td>-0.200782</td>\n",
+       "      <td>-0.200288</td>\n",
+       "      <td>-0.237795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fanconi anemia pathway: RAD51C</th>\n",
+       "      <td>-7.470089e-08</td>\n",
+       "      <td>-0.414730</td>\n",
+       "      <td>-0.440035</td>\n",
+       "      <td>-0.430456</td>\n",
+       "      <td>-0.632182</td>\n",
+       "      <td>-0.405163</td>\n",
+       "      <td>-0.326664</td>\n",
+       "      <td>-0.404697</td>\n",
+       "      <td>-0.420671</td>\n",
+       "      <td>-0.419098</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.418211</td>\n",
+       "      <td>-0.415957</td>\n",
+       "      <td>-0.417600</td>\n",
+       "      <td>-0.409293</td>\n",
+       "      <td>-0.412086</td>\n",
+       "      <td>-0.394902</td>\n",
+       "      <td>-0.401005</td>\n",
+       "      <td>-0.384553</td>\n",
+       "      <td>-0.369725</td>\n",
+       "      <td>-0.432237</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fanconi anemia pathway: FANCI</th>\n",
+       "      <td>-1.061490e-07</td>\n",
+       "      <td>0.003305</td>\n",
+       "      <td>0.027404</td>\n",
+       "      <td>-0.015537</td>\n",
+       "      <td>-0.526704</td>\n",
+       "      <td>0.027307</td>\n",
+       "      <td>0.162895</td>\n",
+       "      <td>0.038285</td>\n",
+       "      <td>0.005416</td>\n",
+       "      <td>0.004532</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.009642</td>\n",
+       "      <td>0.010136</td>\n",
+       "      <td>0.007968</td>\n",
+       "      <td>0.025313</td>\n",
+       "      <td>0.013168</td>\n",
+       "      <td>-0.008716</td>\n",
+       "      <td>0.022125</td>\n",
+       "      <td>0.047733</td>\n",
+       "      <td>0.059667</td>\n",
+       "      <td>-0.011232</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fanconi anemia pathway: BRCA1</th>\n",
+       "      <td>-1.683542e-05</td>\n",
+       "      <td>-0.301273</td>\n",
+       "      <td>-0.283741</td>\n",
+       "      <td>-0.310443</td>\n",
+       "      <td>-0.621120</td>\n",
+       "      <td>-0.270129</td>\n",
+       "      <td>-0.226952</td>\n",
+       "      <td>-0.287605</td>\n",
+       "      <td>-0.293040</td>\n",
+       "      <td>-0.300574</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.298489</td>\n",
+       "      <td>-0.299828</td>\n",
+       "      <td>-0.297885</td>\n",
+       "      <td>-0.286508</td>\n",
+       "      <td>-0.294695</td>\n",
+       "      <td>-0.219823</td>\n",
+       "      <td>-0.307477</td>\n",
+       "      <td>-0.282670</td>\n",
+       "      <td>-0.263800</td>\n",
+       "      <td>-0.323152</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 743 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                           ABAT     ABCA1      ABL1     ACAT1  \\\n",
+       "circuit_name                                                                    \n",
+       "Fanconi anemia pathway: RAD51     -8.689657e-08 -0.386536 -0.401960 -0.396785   \n",
+       "Fanconi anemia pathway: MLH1 PMS2 -8.077751e-08 -0.238399 -0.269825 -0.260549   \n",
+       "Fanconi anemia pathway: RAD51C    -7.470089e-08 -0.414730 -0.440035 -0.430456   \n",
+       "Fanconi anemia pathway: FANCI     -1.061490e-07  0.003305  0.027404 -0.015537   \n",
+       "Fanconi anemia pathway: BRCA1     -1.683542e-05 -0.301273 -0.283741 -0.310443   \n",
+       "\n",
+       "                                       ACHE      ACLY      ACP3     ACTN1  \\\n",
+       "circuit_name                                                                \n",
+       "Fanconi anemia pathway: RAD51     -0.521874 -0.378718 -0.300026 -0.379242   \n",
+       "Fanconi anemia pathway: MLH1 PMS2 -0.431095 -0.230148 -0.084805 -0.233656   \n",
+       "Fanconi anemia pathway: RAD51C    -0.632182 -0.405163 -0.326664 -0.404697   \n",
+       "Fanconi anemia pathway: FANCI     -0.526704  0.027307  0.162895  0.038285   \n",
+       "Fanconi anemia pathway: BRCA1     -0.621120 -0.270129 -0.226952 -0.287605   \n",
+       "\n",
+       "                                        ADA     ADH1A  ...      TPH2  \\\n",
+       "circuit_name                                           ...             \n",
+       "Fanconi anemia pathway: RAD51     -0.389304 -0.389326  ... -0.388704   \n",
+       "Fanconi anemia pathway: MLH1 PMS2 -0.252828 -0.241303  ... -0.242601   \n",
+       "Fanconi anemia pathway: RAD51C    -0.420671 -0.419098  ... -0.418211   \n",
+       "Fanconi anemia pathway: FANCI      0.005416  0.004532  ...  0.009642   \n",
+       "Fanconi anemia pathway: BRCA1     -0.293040 -0.300574  ... -0.298489   \n",
+       "\n",
+       "                                       SYT2     TAAR1  VKORC1L1     TRPV3  \\\n",
+       "circuit_name                                                                \n",
+       "Fanconi anemia pathway: RAD51     -0.389426 -0.388067 -0.384761 -0.384756   \n",
+       "Fanconi anemia pathway: MLH1 PMS2 -0.241099 -0.243983 -0.235437 -0.234357   \n",
+       "Fanconi anemia pathway: RAD51C    -0.415957 -0.417600 -0.409293 -0.412086   \n",
+       "Fanconi anemia pathway: FANCI      0.010136  0.007968  0.025313  0.013168   \n",
+       "Fanconi anemia pathway: BRCA1     -0.299828 -0.297885 -0.286508 -0.294695   \n",
+       "\n",
+       "                                       TUBB     PCSK9     HCAR2     FFAR4  \\\n",
+       "circuit_name                                                                \n",
+       "Fanconi anemia pathway: RAD51     -0.357535 -0.372284 -0.359548 -0.365995   \n",
+       "Fanconi anemia pathway: MLH1 PMS2 -0.267757 -0.214188 -0.200782 -0.200288   \n",
+       "Fanconi anemia pathway: RAD51C    -0.394902 -0.401005 -0.384553 -0.369725   \n",
+       "Fanconi anemia pathway: FANCI     -0.008716  0.022125  0.047733  0.059667   \n",
+       "Fanconi anemia pathway: BRCA1     -0.219823 -0.307477 -0.282670 -0.263800   \n",
+       "\n",
+       "                                     CKMT1A  \n",
+       "circuit_name                                 \n",
+       "Fanconi anemia pathway: RAD51     -0.403467  \n",
+       "Fanconi anemia pathway: MLH1 PMS2 -0.237795  \n",
+       "Fanconi anemia pathway: RAD51C    -0.432237  \n",
+       "Fanconi anemia pathway: FANCI     -0.011232  \n",
+       "Fanconi anemia pathway: BRCA1     -0.323152  \n",
+       "\n",
+       "[5 rows x 743 columns]"
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Define min and max range for normalization\n",
+    "min_range = -1\n",
+    "max_range = 1\n",
+    "\n",
+    "# Apply Min-Max normalization to each row\n",
+    "shap_normalized = shap_values.copy()\n",
+    "shap_normalized.iloc[:,1:] = shap_normalized.iloc[:,1:].apply(lambda row: (row - row.min()) / (row.max() - row.min()) * (max_range - min_range) + min_range, axis=1)\n",
+    "\n",
+    "shap_normalized.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "id": "215d6e5b-b832-4a5e-b646-ded14c3cfed8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "shap_filtered_norm = shap_normalized * shap_selection\n",
+    "\n",
+    "filtered_df = shap_filtered_norm.loc[:, (shap_filtered_norm != 0).sum() != 0]\n",
+    "\n",
+    "filtered_df.head()\n",
+    "\n",
+    "filtered_df.to_csv(f\"{data_folder}/shap_filtered_symbol.tsv\", sep='\\t', index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "id": "621445ac-cd6c-4b4c-b39b-82b116cdf1f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                        circuit_name      ACHE   ADORA2A  \\\n",
+      "0                      Fanconi anemia pathway: RAD51 -0.521874 -0.000000   \n",
+      "2                     Fanconi anemia pathway: RAD51C -0.000000 -0.000000   \n",
+      "3                      Fanconi anemia pathway: FANCI -0.526704 -0.000000   \n",
+      "4                      Fanconi anemia pathway: BRCA1 -0.621120 -0.000000   \n",
+      "5             Fanconi anemia pathway: FANCM C19orf40 -0.644022 -0.457955   \n",
+      "6               Fanconi anemia pathway: FANCM STRA13 -0.690006 -0.546230   \n",
+      "8                 Cell cycle: SMC1B SMC3 STAG1 RAD21  0.000000  0.000000   \n",
+      "9                              Oocyte meiosis: REC8*  0.542976  0.508633   \n",
+      "10  Progesterone-mediated oocyte maturation: ANAPC10  0.000000  0.000000   \n",
+      "\n",
+      "         AHR   SLC25A4     ANXA2     ANXA4  CACNB1    COL3A1      CPT2  ...  \\\n",
+      "0  -0.000000 -0.563521 -0.241056 -0.000000 -0.0000 -0.000000 -0.000000  ...   \n",
+      "2  -0.000000 -0.000000 -0.000000 -0.000000 -0.0000 -0.000000 -0.000000  ...   \n",
+      "3   0.401231 -0.000000  0.000000  0.390779 -0.0000  0.000000  0.397596  ...   \n",
+      "4  -0.064304 -0.000000 -0.000000 -0.047778 -0.4839 -0.000000 -0.022072  ...   \n",
+      "5   0.108820 -0.000000 -0.000000  0.112910 -0.0000  0.000000  0.112814  ...   \n",
+      "6   0.100690 -0.000000 -0.000000  0.063531 -0.0000  0.009537  0.064147  ...   \n",
+      "8  -0.000000  0.376553 -0.226248 -0.000000  0.0000 -0.000000 -0.000000  ...   \n",
+      "9  -0.744842  0.000000 -0.000000 -0.000000 -0.0000 -0.000000 -0.000000  ...   \n",
+      "10  0.000000 -0.000000  0.000000  0.000000  0.0000  0.000000  0.000000  ...   \n",
+      "\n",
+      "        PMEL    SLC1A5     VAMP2     TOP2A     TYK2      TYMS       VWF  \\\n",
+      "0  -0.000000 -0.013489 -0.588002  0.570760 -0.00000  0.535007 -0.000000   \n",
+      "2  -0.000000  0.042820 -0.000000  0.587580 -0.00000  0.477307 -0.000000   \n",
+      "3   0.000000  0.787002 -0.000000  0.727428 -0.00000  0.619025 -0.000000   \n",
+      "4  -0.136791  0.055904 -0.635303  0.615792 -0.00000  0.757284 -0.447168   \n",
+      "5   0.000000  0.389657 -0.493088  0.616645 -0.00000  0.737936 -0.000000   \n",
+      "6  -0.000000  0.309300 -0.522226  0.634868 -0.00000  0.647535 -0.000000   \n",
+      "8  -0.000000 -0.413306  0.000000 -0.722234  0.00000 -0.548652  0.000000   \n",
+      "9  -0.000000 -0.867376  0.000000 -0.604636 -0.00000  0.000000 -0.000000   \n",
+      "10  0.000000 -0.000000  0.000000 -0.299984  0.86833 -0.476531  0.000000   \n",
+      "\n",
+      "    CACNA2D2     RAMP1     SCN3B  \n",
+      "0  -0.000000 -1.000000 -0.534459  \n",
+      "2  -0.000000 -0.917447 -0.000000  \n",
+      "3   0.000000 -0.749312 -0.000000  \n",
+      "4  -0.465851 -1.000000 -0.000000  \n",
+      "5  -0.000000 -0.961777 -0.000000  \n",
+      "6  -0.000000 -1.000000 -0.000000  \n",
+      "8   0.000000  1.000000  0.000000  \n",
+      "9  -0.000000  0.638836  0.000000  \n",
+      "10  0.000000 -0.841079  0.000000  \n",
+      "\n",
+      "[9 rows x 39 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Now filter those circuits that have stability above the desired threshold\n",
+    "stability_06 = stability[stability['stability'] > 0.6]\n",
+    "stability_06.head()\n",
+    "stability_06.reset_index(inplace=True)\n",
+    "\n",
+    "filtered_df.reset_index(inplace=True)\n",
+    "\n",
+    "filtered_df_stable = filtered_df[filtered_df['circuit_name'].isin(stability_06['circuit_name'])]\n",
+    "filtered_df_stable.to_csv(f\"{data_folder}/shap_filtered_above06_symbol.tsv\", sep='\\t', index=False)\n",
+    "\n",
+    "print(filtered_df_stable)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "id": "0c39a98d-eb37-41e0-9964-b9dc3efa134e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>drug_action</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>ACHE</th>\n",
+       "      <td>Inhibitor</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ADORA2A</th>\n",
+       "      <td>Inhibitor</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AHR</th>\n",
+       "      <td>Activator</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ANXA2</th>\n",
+       "      <td>Activator</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ANXA4</th>\n",
+       "      <td>Activator</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        drug_action\n",
+       "ACHE      Inhibitor\n",
+       "ADORA2A   Inhibitor\n",
+       "AHR       Activator\n",
+       "ANXA2     Activator\n",
+       "ANXA4     Activator"
+      ]
+     },
+     "execution_count": 153,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Set back the circuit_name column as index\n",
+    "# filtered_df.set_index(\"circuit_name\", inplace=True)\n",
+    "\n",
+    "# Load annotations of the most common drug-effects over the drug-targets\n",
+    "annots =pd.read_csv(f\"/home/m3m/INFO_PROYECTO/drexml_paper_paquete/data/drugbank-v050110_mostdrugeffects_KDTs.tsv\", sep='\\t', index_col=0)\n",
+    "\n",
+    "## Subset the relevant drug-targets\n",
+    "annots = annots.loc[annots.index.intersection(filtered_df_stable.columns),]\n",
+    "\n",
+    "annots.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "id": "e18b7cb9-3ab5-422a-9a15-881768fc0ead",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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44APFxMTI2dlZffv2Vfv27VWmTBlFRkbq/Pnz+vnnn00+d3fv3q3t27fr5ZdfTrLPixcvaujQocbfL5JUqVIlDRgwQA0aNFD+/PkVEhKi48eP69dff9X+/fslxc4EMnjwYK1du9bkczTOli1bTD6fS5curaFDh8rd3V2FChWSwWDQ9evX5e3trXnz5hmn8A8ODtbnn3+uxYsXp/hcAAAAAM8TRlwDAAAAmWTcuHEmIeorr7yijRs3qk+fPipRooQcHByUN29eNW7cWDNmzNC3334re3t7SbEj+t577z1jOBpfWFiYZsyYYdx3dnbWsmXLNHLkSFWtWlUuLi6ys7OTi4uLqlevrpEjR2rdunXG4FaKnRY2tRFj6fHo0SM5Ojpq9uzZ+uKLL1SzZk25uLjIxcVFzZo106JFi5Q7d25j+zNnzujBgwcqXbq0cVRlyZIl5eDgIFdXVw0YMCBRePL7778nee/FixebhD5du3bVokWL1KZNG7m6usrBwUEODg4qUqSIXnnlFS1atChRAL5p0yazHueDBw8kSWPGjNH8+fPVuHFj4+Ns2LCh5syZY7KWsSR9+eWXxmDvaezZs0fLly837js5OemHH37Q9OnT1bhxY+XNm1cODg4qVaqU3njjDW3ZskVNmzY1qX3UqFFmzQCQ0T777DOTELht27bavHmzevXqZXxP5MmTR61bt9aKFSvUsmVLY9tr166ZBOzpFfdlhapVq+rnn3/WsWPH9Oeff8rLy0tvvfVWlqvXmuLWfJakmjVratmyZXrllVdUpEgROTg4KEeOHKpQoYIGDx6sFStWmHzGrF+/PtWRy+aYNm2ayWjlKVOm6IsvvlDDhg2VL18+ZcuWTU5OTipbtqz69OmjDRs2qF69esb2MTExyX5mxBcSEiKDwaCqVatq48aNGjp0qMqXL6/s2bMrX758at68uRYvXqxXXnnFeE3cyN6kHDx40OTzJG/evFqxYoVGjx6tChUqGPt9+eWXtWzZMvXr1y89T49ZVqxYYRK4lypVSuvXr9fgwYNVunRpOTg4qFChQurcubPWrVundu3amd331atX9d///te4ny1bNn3zzTeaPXu2mjdvrnz58snBwUFFixbVa6+9po0bN+rVV181tj99+rRmzpyZ6n3Cw8P1+PFjFStWTGvXrtXYsWNVpUoVOTk5KVeuXKpXr57mzJmjAQMGmFy3evXqJPuLiorSqFGjTELr119/XWvWrFGXLl2Mr/H8+fOrVatWmj9/vj7++GPjtPMhISF6//33k+w7/vumePHiWrVqlTw9PVWiRAllz55djo6OKlu2rHr37q1Vq1aZrJfu7e1t/HIXAAAA8CIguAYAAMALb8KECXJzc0vX9sYbb5h1D19fXx08eNC4X7t2bX399ddycnJK9pq2bdvqo48+Mu5fv349yVBk//79xuBUkoYNG6aKFSumWE/x4sXVt29fk2OpTeedXoMGDVKLFi2SPJcvXz516tQp0fGvv/5arq6uSV7TqFEj1a5d27h//vz5JKf0jh8S5cmTRxMnTkx1bduRI0fK0dHRpG9z9enTR4MHD072fP/+/U3Wtg4NDdUvv/xidv9JMRgMJl9akKTJkyebBKYJubi4aPbs2SYjic+cOaOdO3c+VS1p5evrazL6tFKlSvr666+TXYPW3t5e06ZNU65cuYzH4s8y8DQqVKigxYsXq0mTJsqRI4dcXV3VtWtXk59XVqrXGkJCQowjQSWpc+fOKY6KLVmypEnwHx0dbfL8pUdwcLAOHTpk3G/atKk8PT1TvCZ79ux69913TY6Z+77Onj27Zs+erYIFCyZ53s7OThMnTlS2bP8/mV38WRvimzdvnsn+tGnTEq2dHsfW1laffPKJmjRpYladaREdHa0ffvjBuJ89e3b98MMPKlasWJLtHRwc9PXXX6tatWpm9T9v3jyTL1h99NFH6tChQ7LtHRwc9NVXX5l8pv/666+6f/++Wff73//+ZxL0JvTee++ZfIEi/owB8W3evNlkWvO2bdtq4sSJsrOzS7bvN954QwMHDjTunzx5Unv37k3ULv4U623btlXOnDmT7TNfvnyJpjRP7jUFAAAAPI8IrgEAAIBMkDCgHD9+vHE0dUp69uypIkWKGPd//fXXRG2yZ8+url27qn79+ipWrJi6detmVk1VqlQx2Y8ffmeUuKlwUxI3pXacWrVqqXr16ileE3/q8sjIyEQjOSMiItS4cWO1bt1abm5u6tatm3LkyJFqvY6OjsapzCWZPUI0V65cGjt2bKrtRo8ebRL2mbO+bEr+/vtvk1Ckfv36KYZEcbJnz66PP/7Y5FhSry1LSji1ecLnJim5c+c2BpXOzs7KkyeP7t69+9S1DB8+PNUpvLNSvdaQcES+OeFvly5dNGPGDK1cuVKHDh0ymZI8PR48eKAePXqoadOmKl26tNlT7Sf8rDP3fd2uXbtkv0ATp2DBgiafR0mttxwUFGQSPtatW1fNmzdP9f7jx483q860OHTokG7evGnc79WrV6rTpmfLls3kS1TJ+ffff7Vhwwbjfrly5cz6GdnZ2WnEiBHG/bCwsGRHrsdXr169VH9XODo6moy4Dw0NNS6TEV/839F2dnZmPV5JGjJkiMkX0JL6HI3/3jHnfdOyZUt9/fXXWr58ufbv32/2F+QAAACA5wFrXAMAAACZIH5oUbx4cdWqVcus6+zs7NSsWTPjSM2LFy8qODjYZARZs2bN1KxZszTXlDDIjT/9bkapXr16qoFx/vz5Tfbr1q2bar958uQx2Q8NDVXevHmN+w4ODukOfeLXGx0dLYPBkOpI7VdffdWsYDxv3rxq3ry5tm/fLil27d+AgIAk10U1x4EDB0z2zf3SghQ7cr1kyZLy9/eXJB09elQRERGphrEZJf7I2Tx58uill14y67p33nlHb7/9tgoVKpQhddja2pp176xSr7XkypVLhQoVUlBQkCTpt99+U/78+TVw4MBkQ/+CBQuqffv2GVZDmTJlNGnSpDRfl7A+cz/r6tevb1a7YsWKGdczjz/VdJzDhw8rJibGuG/u1NsVK1ZUlSpV9Pfff5vV3hwJRwR37tzZrOvq1q2rMmXKyM/PL9k2cZ8hcdq3by9bW/PGSzRs2FDOzs7Gqfi9vb1T/dKTuT+fokWLmuyHhoaajHoOCQnR6dOnjfvu7u6pfmEhTq5cuVS3bl3jete+vr6KiYkxedzly5c3rpe+f/9+ffrppxo1alSi331xnJyc1LFjR7PuDwAAADxvCK4BAADwwvPy8lLXrl3Tda23t3eqo6GuXLmie/fuGfeTmx42OfFH8xkMBp08eTLZqbdTc+PGDZ07d07Hjx/Xrl27TM7FD1YySqlSpVJtE3+aXUkmI8zNveZp1mcOCwvT5cuXderUKfn4+OjUqVMm52NiYlKcLlYyP0CRYkeYxwXXknT27Nl0B9cnT5402Tcn9I+vTp06xuA6PDxcFy9eVNWqVdNVS1o8fPhQAQEBxv2qVaum+hzHSfilhadVvnz5VEdbZ6V6ral379763//+Jyn2PTd79mz99NNPqlevnpo2barGjRvLzc0t1S96ZIZ79+7p4sWLOnHihMmXDiTzP+viz76QkvgjbpMKxc+ePWuyb+6021Ls50VGBtfxPzMcHR1VqVIls6+tXbt2isH1sWPHTPbT8rsuW7ZsKleunPHz9/jx46lek56fj6RES0scP37c5DWRnt/RccF1SEiILl++bLIUQ+/evY3BtRS7ZMCqVatUq1YtvfTSS2rcuLGqVatm9mcKAAAA8DwjuAYAAAAsLOHUsdu3bzcJo9Pq9u3bKZ6PiorS6dOndfbsWV29elUBAQG6du2a/P39jaPZkvI04W9y4o8MN5ezs3OG1yHF/hxOnDihS5cuKSAgQIGBgQoMDNSNGzdSfOzmPC+prSkeX8KQ+vr162Zfm1BwcLDxz/b29mkOwBOuDZtZ01gnfMzFixfPlPsmxZyR0FmpXmt6++235e3tbTKDREREhA4cOGAc/Z8/f341atRIzZo1U/PmzS0e3F+6dEmnT5+Wn5+f/P39de3aNQUEBJi8NxIy97MupbWI44s/ujapvuOvDS6l7fWT2jTeaRX3RRUpdiRyWsLSMmXKpHg+/hTkUuxsA+l17949RUVFJfqSUnypfeEkTsJR3wm/uJCw7oULF2rhwoXmFZqE27dvmwTXnTt31oEDB0yWG4iOjtbRo0d19OhRzZw5U7ly5VKDBg300ksvqWXLls/8DA0AAABAehFcAwAAABaW0WtH379/P8nj9+7d09y5c7V27VqTEd7JyZYtm0WmB48vPdNOZ/RozT///FNz587V0aNHzQqs0vO85MqVy+y2CcOwhw8fpule8cV/LZgb4sSXsG5LrHOelISPOT21ZxRzgtWsVK81OTg4aO7cuZo9e7YWLFigx48fJ2pz9+5dbdq0SZs2bVK2bNnUpEkTDRs2TLVr186wOiIjI7VixQotWbJEV69eTbV9ej/rsmfPno7qEnua14+54bm54q/vndbXcWqfc+auHW6uBw8eJDudthQ7YjwjZHTdSf2Onjp1qqpUqaJZs2Yl+Zn/77//avv27dq+fbtsbGxUr149DRw4MN2zqwAAAADPKoJrAAAAwMIyOhxOKiw6evSohg8fnmJgnSNHDlWqVEk1atRQw4YNZWdnp7fffjtDa0vImlMGR0RE6NNPP9XatWuTbWNjY6MSJUqoatWqqlOnjpo3b66JEyfKx8cnTfdKaVRgQgmnqX2a6WGfdnr3hNdn1lS1WWEq6Tj29vaptslK9Vpb9uzZNXr0aPXv319bt27Vjh075OPjY7K2cZyoqCj9+eef+vPPPzVw4ECNGzfuqe9/69YtDRs2zLimdFLs7e1Vrlw5Va9eXe7u7mrevLkaNmz41Pe2howKz5OS8LMoNal98Sejf9eFh4dnaH/JyYzf0ba2turfv79ee+01Y0B98ODBJGdBMRgM8vHxkY+Pjzp06KAvv/ySacQBAADwwiC4BgAAACws4Si1MWPGaPDgwRnWv7+/vwYNGqRHjx4Zj+XOnVvNmjVTzZo1Vb58eZUtW1aFCxc2uS7huq/PmylTpiQKrWvWrKmGDRuqUqVKKlu2rEqXLp1o1F5awxxJevTokdnTosf/OUlpG62dUPzRwukZuZ1wpKGlpmlPKOFjTvicZDVZrd7IyEir3l+S8ubNq969e6t3794KCwvTsWPH5O3tLW9vb506dSrR+2j+/PkqVqyY+vbtm+57RkREaMiQISZrRtvb26tRo0Zyd3dXhQoVVLZsWRUvXtzkyyTpeU9npIQjm//9999E6y4n52lmZEhK7ty5defOHUlpfx2nVkvC98mBAwdUoECBtBVoBQnrnjFjhtq3b2+Re+XIkUNdunRRly5dFBkZqRMnTujw4cPy9vbW8ePHE30BZNOmTSpcuHCGfOkDAAAAeBYQXAMAAAAWlnCq04CAgAzt/8svvzQJINq1a6fJkyenOg1sRgciWcm5c+e0bNky436OHDk0a9YsNWnSJNVr0/O8XLt2zez1pa9cuWKyX6pUqTTfL078UCgyMlIBAQFpWuc6YS1FixZNdy1pkfA9ERgYaPa1MTExunbtmlxdXc0aLZ0RLFVvwpHc5gasyS0XYC1OTk5q0qSJ8f314MED7dy5U/PmzTN5jc2ZM0e9evVK9+jR5cuXm4TWFSpU0OzZs1WyZMkUr7P2Z13CNa3/+eefRF8kSk7C9ZefVpEiRYzB9bVr1xQZGWn2+yi1310J3yf+/v7PRHCdVN2Zwd7eXu7u7nJ3d9eIESMUGhqqP//8UwsWLNCJEyeM7ZYuXapBgwYpb968mVIXAAAAYE221i4AAAAAeN5VqFDBZFTv4cOH03T9nTt3FBgYmOR0piEhIdq9e7dxv3Dhwvryyy/NWrvUz88vTXU8S9avX2+yP2bMGLNC6+jo6EThjDnrYp86dcrs2uIHEnZ2dqpevbrZ1yZUo0YNk/2jR4+afa3BYNBff/1l3Le3t081AMwoefLkMQnz/v77b7OnPb9w4YJat26tGjVqqEWLFlq1apWlyjSyVL0JA9ykpg1OSmYFawlFR0fL399f3t7eKbbLnTu3unbtqtWrV6t06dLG47dv39b169fTff+E7+sZM2aY9ZpNuA62Oe/pjFStWjWT/ePHj5t9bVo+W8xRq1Yt458jIyN1/vx5s689efJkiucTfpaldVaPK1euKDg4OE3XZISEn6NprfvGjRu6efNmsp8JBoNB169f18GDB1P83HB2dla7du20bNky1a9f33g8PDxc586dS1NNAAAAwLOK4BoAAACwMAcHB9WpU8e47+/vn6bw+uOPP1arVq1UvXp1NW/e3CT49Pf3Nwm0a9euLQcHB7P63bFjh8m+tafTzUgJQ/kGDRqYdd3+/fsThYfmBJSbN282q//bt2/r4MGDxv06deood+7cZl2blISPKy0h7qFDh3Tt2jXjfq1atRJNm25J7u7uxj8HBweb/Z7Yt2+fpNify40bN+Tq6mqR+hKyRL05cuQwaXv79u1U+wsLCzP5DHha5q7fPXnyZNWsWVMvv/yy+vfvn2ia+aQ4OzurZcuWJsfiRvumR/z3db58+VShQgWzrvvjjz9M9p92bfi0atSokcmo5rVr15oVnt+9ezfNX3RKzUsvvWSyb+5n16VLl0xGuyelfv36Jq+ntWvXmr1+9K1bt9SpUyc1atRINWvWVL9+/cy6LiO4urqazHxx5MiRRF92SI7BYNDgwYPVvHlzVa9eXR4eHrpx44bx/Lx581SrVi21bNlSAwYM0MWLF1Pt087OTm3atDE5dvfuXfMeDAAAAPCMI7gGAAAAMkHPnj1N9r28vBQeHp7qdQcPHtSePXskxYYtERERqlKlivF8wvDj3r17ZtWzePHiROFXSuvmxl8vNrNDn4xgzvPy8OFDff7554mOm7Oe8NmzZ7Vly5ZU202bNs0kyOnevXuq16SkZs2aqly5snH/yJEj2rRpU6rXRUREyMvLy+RYly5dnqqWtEr42L/99ttUX1vh4eH69ddfjfv58uUzGZloSZaoN+HU7Am/TJKUuXPnmj0y2xzx39tS8u/vEiVKGN8LMTEx+u2338zqP+G06kWKFElHlbHif96FhoYmWg84KadPn9aiRYtMjmX2GuF58+bVyy+/bNy/cuWKyesiOf/73/8yvNaXXnpJxYoVM+4vX77crKnvv/zyy1TbFClSRM2bNzfuBwQEaP78+WbV9dVXXxkfa3h4uMnvucwQ/3d0dHS0Jk+ebNbvulWrVunChQuSpKioKOXKlcvkNV6mTBmT3/XLly83q56EP5PM+oIOAAAAYG0E1wAAAEAmaNOmjcnowHPnzmnEiBEprr16/vx5jRkzxuTYkCFDTEbulSpVymT/yJEj8vX1TbZPg8Ggn3/+OckQIqUg3dnZ2fjnrLa+blLKlStnsv/jjz+mOKLc399fb775ZpIBjjlfMJCkTz75JMXn/rvvvtPGjRtNauzYsaNZfadk2LBhJvsTJ040mT4+oUePHmn48OHGsEWKDVc6der01LWkRb169VS7dm3j/l9//aVPPvkk2Z9TZGSkJkyYYDLVdL9+/cyeYeBpWaJeNzc35cuXz7h/4MABbdu2Ldka1q5dq3nz5j3Nw0gk/ntbSv793bFjR2XPnt24/91336X4epekvXv3aufOncb9ypUrP1UAV758eeOfw8PD9fPPP6fYfv/+/RoyZEii8Nfc93RGGjFihMmXBKZOnWry3CS0cOFCrVixIsPrsLW11TvvvGPcDw0N1bBhw5Id7W8wGOTl5aU///zTrP6HDh0qW9v//6emmTNnphrSz5071+Sz0dHRUQMHDjTrfhmlZ8+eJmtd79u3Tx9//HGKX444fPiwJk+ebHJs+PDhJvvNmjVTwYIFjfvLly/X1q1bU6zl7NmzJj/7/PnzJ5rOHAAAAHheEVwDAAAAmcDW1lYzZ840CYn27dunV155RfPnz9fly5f1+PFj3bt3TydOnNCkSZPUvXt3k/U+69Spo759+5r0mzNnTpOpeGNiYjRo0CD9+OOP8vPzU0REhB4+fKiLFy9q6dKl6tq1q6ZNm5bkKL6UQvT4//B+8OBB7dixw9j348eP0/WcWFLHjh1Npqzdt2+f+vfvr7179+rBgweKjIzUzZs3dejQIX388cfq3LmzTp8+nWRfISEhZt3z4cOHevPNN/XFF1/o7NmzCg8P14MHD/Tnn3/qjTfe0KxZs4xt7e3tNXXq1ERrHKdH27Zt1aNHD+N+WFiYhg0bpjFjxujQoUO6f/++IiIi5O/vr19++UXt27fX3r17je0dHR315ZdfZloAHF/C9dhXrVolT09Pbdy4UUFBQYqMjNSdO3e0ZcsW9ejRw2RUe4UKFTRo0KBnul5bW1uTke4Gg0FjxoyRl5eXLly4oLCwMAUFBWnPnj0aMmSIxo8fr8jIyEQjtZ9Gzpw55eTkZNzfuHGjfH19FRUVpX///df4WZEvXz7179/f2C4sLExvvvmmPv/8c/n6+urff/9VdHS0QkJCdPz4cU2ePFnvvPOOcdSqjY2NRo8e/VS1JvxyxcyZM/XJJ5/o5MmTCgsL0+PHj+Xv76/ff/9dQ4YM0dtvv53k1OTmvqczUrly5TRq1CjjfkREhIYPH64JEyboxIkTCgsLU0hIiA4dOqShQ4caZ0RIOCI+I3Tv3l0NGzY07l+4cEEdOnTQzz//rH/++UcREREKDg7W9u3b1atXLy1cuDBRH8lNMV+7dm2T8DYmJkb/+c9/9Oabb2rbtm26c+eOIiIiFBgYqO3bt6t379765ptvTPoYN26cChcunDEP1kwuLi765ptvTD6T16xZow4dOmjZsmUKCAhQRESE7ty5Ix8fH40ZM0ZvvfWWyZcg2rVrZzKyXopdKmTkyJHG/ZiYGL333nsaO3asDh48qHv37ik6OlqPHj3S33//rf/973/q06ePwsLCjNeMHDnSKp/PAAAAgDVk/P8BAQAAAEhS+fLlNXv2bL333nvGUY23b9/Wl19+meo0rJUrV9asWbNMRlfHGTdunI4cOWKcDjs0NFTTp0/X9OnTU+yzdu3a8vf3N66dGRAQkGzbmjVr6vLly5JiR5LGDyamTJmibt26pXivzFapUiX1799fCxYsMB7z8fGRj49Pitc5ODioYcOGJsFuQECAypQpk+J19evXl4+Pj6KiorR48WItXrw4xXvMmDEjQ0fQffLJJwoNDTVOE24wGLRp06ZUpw3PmTOnZs2aZbXRfCVLltTs2bM1cuRIPXjwQFLsTANjx45N8bpixYpp7ty5mR7mWKLeoUOHaufOnfrnn38kxb6/Fi5cmGRYGNfXzJkz9dprrz3dg4mnRo0a8vb2lhQb6sb/gsyiRYuMIeeIESN04sQJ47rLUVFRWrp0qZYuXZrqPSZMmGAyjXR69OrVS5s2bdLx48clxb7Of/vtt1SnLS9UqJBcXV118uRJSbHrBYeGhiYabW5pgwcPVlBQkJYsWSIptv41a9ZozZo1SbbPmzev+vbtq++++y7Da/n222/1+uuv6/z585JiR9pPmzZN06ZNS7J90aJFlS9fPuMXfFJ67w0fPlwhISEmr+HDhw+btV734MGDE31BK7M0atRIX331lSZMmGD8QtY///yjSZMmpXpt48aNNWXKlCTP9ezZU76+vtqwYYOk2J/7xo0bTUaZJ+fNN99U7969zX8QAAAAwDOOEdcAAABAJmrUqJFWrlypFi1amNXe1tZWffr00S+//KICBQok2aZEiRJauHChSpcubVafzs7OGjVqlJYsWaLq1asbj585cybZkYijRo0yGXUdX1zwkdV8+OGHGjRokMm0tSmpXbu2Vq5cqXfffdfkuDlhi6enp6ZOnaocOXKk2K5q1apauXKlWrdubVZN5nJwcNA333yjTz/91GS625S0atVK69atU6NGjTK0lrRq0KCBVqx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+      "text/plain": [
+       "<Figure size 2000x2000 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## CREATE THE HEATMAP FUNCTION ###\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "drug_action_colors = {\n",
+    "    \"Inhibitor\": \"magenta\",\n",
+    "    \"Activator\": \"cyan\",\n",
+    "    \"Ligand\": \"yellow\",\n",
+    "    \"other\": \"grey\",\n",
+    "}\n",
+    "drug_action_colors_list = [drug_action_colors[action] for action in annots[\"drug_action\"]]\n",
+    "\n",
+    "# Separate the circuit names from the data\n",
+    "filtered_df_stable.set_index(\"circuit_name\", inplace=True)\n",
+    "circuit_names = filtered_df_stable.index\n",
+    "heatmap_data = filtered_df_stable\n",
+    "\n",
+    "sns.set(font_scale= 1.5) # Adjust the font size if necessary\n",
+    "\n",
+    "# Create the clustermap\n",
+    "cluster_map = sns.clustermap(heatmap_data, cmap=\"RdBu_r\", col_colors=[drug_action_colors_list],linewidths=0.5, cbar_kws={'label': 'SHAP Value'}, figsize=(20,20))\n",
+    "\n",
+    "# Add titles to the x and y axes\n",
+    "cluster_map.ax_heatmap.set_xlabel('Drug-target')\n",
+    "cluster_map.ax_heatmap.set_ylabel('Circuit name')\n",
+    "\n",
+    "\n",
+    "# Add a title to the color legend\n",
+    "ax = cluster_map.ax_col_colors\n",
+    "# ax.set_title(\"Drug Action\", pad=10)\n",
+    "legend_elements = [\n",
+    "    plt.Line2D([0], [0], marker='o', color='w', markerfacecolor=color, label=action, markersize=15)\n",
+    "    for action, color in drug_action_colors.items()\n",
+    "]\n",
+    "legend = plt.legend(handles=legend_elements, title=\"Drug Action\", loc='upper left', bbox_to_anchor=(15, 1))\n",
+    "legend.get_frame().set_facecolor('white')\n",
+    "\n",
+    "# Add a title to the entire figure\n",
+    "plt.suptitle('Heatmap of circuits and genes', fontsize=30, y=1.05)\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.savefig(f\"{data_folder}/heatmap_circuits_KDT_drugeff.pdf\", dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "id": "13cbc7c9-767b-457d-b034-a432082d3384",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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nztXChQtlMBj0wQcfaOzYsZmeiq5SpYpGjBihjz/+WN7e3vrpp580a9asApft9ttv19ixYyXZEss5TV1y6tQp/f777zIYDHrjjTdkMBj0559/6u+//872mMWLFys2NlYVKlTQpEmTHIlqSapevbqmTJmi0qVL69SpU1q9enW257Enye+66y61adNGUuH/uwAA4AyuaDs4U9myZTVu3DjHk1o5JbYBAHCHadOmSZLeeustPfjggxkS1ZJUq1Ytff755ypXrpwuXbqkOXPmOOW6M2fOdCSqP/vsM73xxhuZkuC33nqrZs2apaZNm8psNmvo0KGOWc8kac6cOUpLS1O9evU0duxYx4CzJNWrV0+TJ0+Wl5eXdu3apR07dmQ4t33a8oiIiAKV32q1OgbJn3zySdWsWVMpKSmO1/Jq/PjxOnXqlAICAgpUDgAA8qOgfWb7jJoLFizIMKtIfowePVphYWFKSUnRm2++qbS0tAzbP/zwQ+3evVshISGaOHGifHx8sj3Xtm3bdPz4cZUqVUo9e/aUJP3888+6ePFitscUpt0AFBUkqwE3u7bj17JlSz300EOSbHc/7969u9DnHzZsmEJDQ5WWlqbp06dn2JaYmKhJkyZJkl544YVcn3h66KGH1K1bN0m26dNOnDhR4HLdc889CgwMlCQdOnQo2/2WLFkiq9WqBg0aqG7duo4pR3Ma9P79998lSW3atMnyie3Q0FDdcccdkqQ9e/ZkeY4rV644EtnX/ndZv369zp07l9vbAwDAZVzddnAWX19f3XvvvZKkw4cPe7g0AIAbWUJCgqP/mtP01aGhofrXv/4lSfrrr78Kfd1z587p008/lWTrc+c0xae3t7dGjRolg8Gg8+fPa+XKlY5t9j7uQw89lCnJLtkS7fYpvvfu3Zthm/3J6oImq7dt26aTJ0/Kx8dH9957r6PdsWDBgjyfY+PGjVq4cKFuu+02R/0CAOAqhekzN2rUSLfddptjOvDExMR8X79s2bKaMGGCDAaD9u/f72gLSNKaNWv03//+V5ItqV2tWrUcz2VforJFixZq3LixqlSpIpPJlGGK8+sVpt0AFBUkqwE3++2333Tq1CkZjUa1a9dONWrUUMOGDSU55ymkgIAAdejQQZL0yy+/ZNi2bNkyJSQkyNvbW927d8/T+fr06SNfX1+ZTKZCPcFlMBhyXfv52obF/fffL0l69NFHJUkrVqzItrHQu3dvTZgwQU8++WSO55bkWO/jeqtWrVJSUpJKlSqlZs2aqWnTpgoLC5PZbHZMmQ4AgCe4uu3gTPYlQ5jyEwDgSd7e/6x6t27duhz37devn77//nt99NFHhb7ud999J5PJJC8vL7388su57h8eHq4PPvhAixYtyrD01fDhwzV27Fi1a9cu22PtsdZsNjteu3Llik6ePClJql27doHfg2S74bxUqVJ67LHHJElHjx7Vb7/9luvxCQkJGjFihHx9fTV+/PgM/y0AAHCFwvSZjUajxo8fL19fX505c0YffPBBgcrQvHlzx0NfX3zxhXbu3KmYmBgNHz5ckvTcc8/pgQceyPEciYmJ+vHHHyVJ7du3l8Fg0MMPPyzJdtNYduPaBW03AEUJyWrAzewdv7vvvlsVK1aU9E9CdtWqVYqPjy/0Ne68805JUkxMjKKiohyv26cyqVOnToapsnMSHBzseEpqw4YNBS7Thg0bdOXKFUm2acGzsnXrVp0+fVoGg8FRJw8++KB8fHyUlJSk5cuXZ3lcw4YN1bFjx2zvmI+NjdW2bdskKdOaIXb2/y7t27eXr6+vjEajozGwaNEipaen5+2NAgDgZO5oOzhDamqq1qxZI0mOGU0AAPCEwMBAxyxdkydP1ttvv63t27dnOUAbFham8PBwlS1bttDX3bp1qyTblJt5Xf+6Q4cOatiwYYakbtOmTfXkk09m2389fPiwjhw5IiljH/fgwYOyWq2qUKGCLl26pI8//livvvqqXn75Zb3//vvauXNnjmVJTEzUTz/9JOmftkatWrVUr149SXmbTvX9999XdHS0+vXrV+CEOQAA+VHYPnPt2rXVt29fSbak8ObNmwtUjsGDBysiIkIWi0UjR47UsGHDdOnSJdWtW1dDhw7N9Xj7w1SBgYGOJSrtN42dOnVKmzZtyvK4grYbgKKEZDXgRpcvX9bPP/8sSY6nnyVb8PT29lZqamq+14HKyrXTiVy7brV93ee6devm63z2jmlUVFSmNTdyY7FYtHHjRkdArlWrVrZ3kV27ZnSVKlUk2aZla9GihaSCr805ZswYJScnKyAgIMtrR0VF6Y8//pCU8b+L/fvz58/nejc+AACu4K62Q2GdOXNG/fr109mzZ2U0GtWrVy9PFwkAcIMbOXKkAgMDZbVatXTpUv3f//2f7r77bvXo0UMzZszQn3/+me0TSgV19OhRSfnvc+eH2WzWe++9J0mqWLGimjVr5thmnwI8ISFBjzzyiKZNm6aNGzfq119/1dy5c/Xcc89p2LBhMplMWZ77+++/d/Sd7bOdScowe9uFCxeyLdvPP/+sFStWqFGjRnmezQ0AgMJwVp/5lVdeUYMGDSRJI0aMKNB04L6+vvroo4/k5+enyMhIbdy4UYGBgZo0aZJ8fX1zPf7ah6kCAgIk2doU9qU9CjI2nlO7AShKmIsHcKOVK1cqJSVFfn5+GZKm5cqVU7NmzbRp0ybNnz9fL774YqGuExQU5Pg+Li7O8f2lS5ckSWXKlMnX+cqXLy/JFtzi4+MVFhaWYfvGjRvVpUuXTMclJSXp7NmzjrvX6tatqylTpsjHxyfTvgkJCVk2LOw/r1u3TgcPHtSuXbvy9bTW1KlTHWt/vfbaaypXrlymfewNgUqVKunuu+92vH7bbbepVq1aioyM1Pz58zN01gEAcAdntB3q1KnjlLJkFetNJpNiY2N1+vRpSbYn2d599101adLEKdcEAKCgbr31Vi1atEijRo1yPFGcmJioDRs2OGYNK1eunDp37qxevXo5BoULw973zetT1flltVr1zjvvaPv27ZKkt99+O8Pgtz1ZnZqaqmeeeUZdu3ZVjRo1dOHCBS1btkxTpkzRd999J19fX7377ruZzm/vG7dr1y7DuMKjjz6q//znP441M3v27Jnp2NjYWL3zzjvy8/PTuHHjslwzEwAAZ3PWeLuXl5fGjx+vTp066ezZsxo/frxGjx6d7/JERESoW7dumjlzpiTp6aef1s0335zrcUePHtWuXbskZR4bf+yxx/TRRx9pw4YNOnfunCpVqpSnsuTWbgCKEp6sBtxo8eLFkqS2bdsqODg4wzZ7EIqKisrTOlA5ye4uaftT0Vkli3NybSczqzUoL168qD/++CPT14EDB2QwGPT444/r008/1eLFizM89X2tlStXKjU1Vb6+vnrwwQczbGvXrp2jvvJzB9lnn32mTz75RJKtzl999dVM+5jNZi1btkySrQNuX2vTzj7Vyq+//qoTJ07k+doAADiDM9oOjRs3zvXr+nNnJatYv2fPHp0+fVotWrTQm2++qZ9//lmPP/54Id4xAADOEx4erm+++UZLly5V3759dccdd2ToD1+8eFHTpk1Thw4ddO7cuUJfz57wdsUyUmazWSNGjNCiRYskSV27dtUjjzySYZ+77rpLTz/9tEaNGqX33ntP4eHh8vX1VZUqVdS7d2+NHTtWkq1fffjw4QzHRkZG6s8//5SUeZC8QoUKuueeeyRlv2bmO++8o4sXL2rAgAGqVauWc940AAC5cOZ4e3h4uPr16yfJtizkxo0b812eixcvaunSpY6fFy5cqOPHj+d6nP2GsbCwMEfMtevQoYOMRqPMZrMWLlyYp3Lkpd0AFCUkqwE3OXLkiP766y9JmTt+knT//fcrMDBQkjRv3rxCXevy5cuO70uXLu343v5EdX7XtrQ/kW00GrN8KrtTp046ePCgDh48qAMHDuj333/XkCFD5O/vr/j4ePn6+qpNmzY53lltD8itW7dWSEhIhm1+fn5q3769JNvaHdc+LZ6V9PR0jRo1SpMnT5YktWjRQpMmTZLBYMi076ZNm3T+/HlJWf936dChgwwGg6xWa4GnIQcAoCCc1XaYN29erl+33nprruWxx/qDBw9qz549mj17tmOpkOjoaN13332O2VgAAChK6tWrp379+mn+/Pnavn27Zs+erZdfftkx89aJEyc0YMCAQl/HPguZvQ/tLElJSerbt6++/fZbSdITTzyhYcOGZdqvQ4cO+ve//63nnnsuy/N06NBBNWvWlNVq1Zo1azJss5+7XLlyat68eaZj7TejnT59OtOamcuWLdNPP/2kO+64Qy+99FL+3yAAAAXgivH27t27O6YDHzlyZIZx9txYrVa99dZbiomJUXh4uMqWLaukpCQNHjw4xxvZrn+Y6vox9EqVKjlmL1u0aFGuN8Xltd0AFCVMAw64iT04SFLv3r1z3HfNmjW6cOFCgQd8IyMjHd9fe0dz3bp1tXnzZsfUYHllX+u6Zs2auU4VYjAYVKZMGb300kuqU6eOXn31VS1atEgXL17UZ599lmXC+tChQ9q7d68k6aeffspxulL7OiPZdYATExPVv39//frrr5Kkhx9+WBMmTMi23PYkuZR1o+Zaixcv1uuvv850KQAAt3Bn2yG/fH191bx5czVq1EhdunTRoUOH9MILL2ju3Lk8TQUAKNICAgLUvHlzNW/eXAMGDNCwYcP0/fffa/fu3dq3b5/q169f4HPffPPNOnr0qA4dOpTnY06ePKlSpUplu1xXdHS0evXqpf3790uSXnzxRQ0ZMiTLm7Hzol69ejp27JhOnTrleC09PV3Lly+XZHsiLLc6mDdvnlq1auUo35gxY+Tv769x48Zlmq0MAABXcUWf+drpwM+dO6dx48Y5ZibJzcyZM7V582YZjUb9+9//1tmzZ/XGG2/or7/+0uTJkzVw4MAsj9uwYYNiYmIkSV9++aW+/PLLbK9x/vx5rV271vFg1/W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4ceO0fPlyXX/99cXMddnhzetnMpkkSWfPnlVmZmaOdQkJCbLZbLJYLEUe1r40EaxGmbd7925JUnR0tMf1jRo1kiTt2rWrUPvbsWOHJOcf9jPPPKNevXqpVatWuvHGGzVt2jRlZGR4IdflV2xsrCTl6MWX3eV8Hq59eeuzxUXe/JwyMzO1b98+hYeHa82aNbrrrrvUvn17tWvXTg888IB+//1372UcKMe8WZ5t3bpVktSmTRtJzhbmkyZN0ujRozV16lQdOnTICzmG5P37EEmy2+3697//rbS0NL3xxhvMK14CvPm59enTR3PmzNELL7zgcf2WLVskqVzOjVdSuJ8ECo/6cPnBd1/5Rh0c8IzvvuLz5jV86aWXtGDBArVv397j+r/++ktS+arbePP6uezZs0evvfaa2rZtq2HDhhU/k2WcN6+hq+FkmzZtlJqaqu+//17jxo3Tiy++qG+//Vbp6eleynXZ4c3r17JlS0VEROj06dMaMWKE9u/fr/T0dP31118aPny4JGno0KHy8/PzUu59J3fXAKCMcfVSqV69usf1ruWnT5+W3W7P1bLuUq7WqZMnT1bNmjXVokULnT59Wtu3b9eUKVO0bNkyffrpp1dk6xNfKOjzqFatWo50vtoXcvLmtd21a5eysrKUmJioUaNGqU2bNurUqZP27t2r1atXa/Xq1RoxYoQefvhh750AUA55szxzBaMrV66sp59+WosWLcqxfurUqXruued03333eSPrFZq370Mk6YMPPtCff/6pkSNHqmnTptq5c6f3MgxJJfO5eZKamqqpU6dKkgYOHFikfVRE3E8ChUd9uPzgu698ow4OeMZ3X/H56ryXL1+ujRs3ymKxeOwBeqXy9vXLyMjQM888I4vFov/973/unq7lmTev4cGDByU5ewHfdNNNOnr0qHvd7Nmz9f7772vatGlq3LhxMXNddnjz+gUGBurdd9/V008/rZ9++kk//fSTe11AQIDGjRunO++80wu59j2C1fC5YcOGacWKFYVKu2zZMqWmpkpy/iF6kr03Umpqar6V6rS0NB04cECSNGLECD344IPuyvz+/fv1xBNPaMuWLZowYYImTJhQqDxWNK7PI/tcadm5lrvSFWZfeX22l7Mv5OTNz8nV+6JatWqaOnVqjmHIvv/+e40ePVqTJ0/WNddcow4dOhQ368AVozTLs/Pnz0uSZs6cKavVqrFjx6p///6y2Wz64Ycf9NZbb2n8+PGqUaNGuapkekNpfm6Ss6X6u+++qy5dumjo0KGFygdK/3PzJDMzU08//bSOHTum+vXr0zjkMnA/iYqM+nDFxXdf+UYdHPCM777i8+Y1zMvu3bs1atQoSc75rD1NPXKl8vb1e/311xUbG6vXXntNtWvX9k4myzhvXsPk5GRJ0rPPPquaNWvqiy++ULNmzXTkyBG98cYbWrlypR566CEtXLiw3DSe9Pbv4FVXXaWbb75Zn3zyiZo2baqoqCjFxsYqLi5On332mZo3b35FTiNCsBo+V6VKlUIXeGazucDWSQaDwf3e4XDkmzYwMFCrV6/W6dOn3cMruDRo0ECvvfaaBg0apHnz5un5559XpUqVCpXPisRkMslut+e47p4U9Fm49iXJK/tCTt78nO644w51795dJpMpVwuwv//979q+fbtmzpypGTNmUFFGhVKa5ZlriM6kpCRNmjRJN910k3vdQw89JIfDoUmTJunNN98kWH2J0vzckpOT9eyzzyokJESvvfZagd/RuKg0PzdPUlNT9eSTT+q3335TeHi4pk6dmudDM+TG/SQqMurDFRfffeUbdXDAM777is+b19CTLVu26OGHH1ZiYqJ69eqlf/3rX0XaT1nlzeu3YsUKffHFFxo4cKBuvfVWL+Ww7PPmNXQ9z/Lz89OMGTPc95tNmzbVtGnT9Pe//12xsbGaPXu2HnzwweJnvgzw5vWLj4/XP/7xDyUkJOjjjz9Wly5d3Nt+/vnnmjhxoh544AEtWLBANWrU8Er+fYVgNXzucltoBwcHS1Ke8xVkX55X65TswsPDFR4e7nHd1VdfraioKB0/flw7duxw/7HjouDgYCUmJiotLc3jetfnUZgHtq7P1hv7Qk7e/JyMRqNq1qyZ5/o+ffpo5syZ7jl0gYqiNMsz199ulSpVcgSqXe69915NmjRJe/fu1cmTJ91DCqF0P7dx48bp8OHDevvtt/Mc/gmelfb9Y3bHjx/XY489pu3btysyMlIfffSRGjZseFn7qOi4n0RFVtrfZ9SHSw/ffeUbdXDAM777is+b1/BSS5Ys0ciRI5WWlqa+fftqypQpRZ4iqazy1vU7ffq0Ro0apRo1aujll1/2ej7LMm/+DgYEBCglJUW33nprroaRZrNZd911l8aNG6c1a9aUm2C1N6/flClTdOTIEY0aNSrHvbrBYNDQoUO1bds2LViwQJ999pl7tIQrRfn65kG55HqYe+rUKY/rT5w4IUmKiIiQxWIp9vFcLU7y+vKo6FwBj7w+D9fcCoUJjHhzX8jJl9c2KipKEn8zQEG8WZ5VqVJFkvIccio4OFgRERGSnPMAoei89blt3bpV8+fPV0hIiH7++Wc9++yz7teUKVMkOT8r17KzZ896+UwqlpK6f9yyZYsGDx6s7du3q2HDhpo1a5aaNm1a/AxXMNxPAoVHfbj84LuvfKMODnjGd1/xldR5v/fee3rqqaeUlpame+65R2+//bb8/PyKl9kyyFvXb+rUqTp79qzCw8M1bty4HHX6VatWSZJ++uknPfvss3r//fe9eAalz5u/g1WrVpWU9/Ms1/Ly9CzLm9dv9erVkqTrrrvO4/qePXtKkrZt23a52Sx1BKtR5jVp0kSStGfPHo/r9+7dmyNdftauXZvjobAn8fHxknTFDZPgK67r7Lrul7qcz8Ob+0JO3ry2U6dO1b/+9S+tWbPG4/rjx49LulhhBuCZN8szVxrXA+pLZWVl6dy5c5IuBrZRNN763FxzDyUnJ2vBggU5Xr/++qs7jWtZeZtnzde8+ffmEhMToyFDhujUqVPq1q2bZs+erTp16hQ/sxUQ95NA4VEfLj/47ivfqIMDnvHdV3zePm+73a6RI0fq7bffltFo1OjRo/Xiiy8WOPXIlcpb189VR9+5c2euOv3+/fslSbGxsVqwYIE7oFhelMTfcV7Ps1wBXVcHjPLAm9fP9azPbPY8aLbr79hqtV52PksbwWqUee3atVNISIi2bt3qviHPbsmSJZIuthrJj8Ph0IIFCzRjxgwlJyfnWr9mzRodP35cUVFR5e7Gxlt69OghSVq6dGmudQkJCfrjjz9ksVjUrVu3Qu/r559/lt1uz7EuKytLy5YtkyT16tWruNmucLz5OR04cEBLly7V999/73G9a3lh/gaBisyb5VmnTp0UFBSk48eP688//8y1ftWqVbLZbGrYsKG71SqKxlufW6dOnbR7926Pr3nz5kmSatWq5V6WVytjFI43/94k5z3iE088ofT0dN1+++2aPn26wsLCvJnlCoX7SaDwqA+XH3z3lW/UwQHP+O4rPm9eQ0kaM2aMvv/+ewUFBen999/Xfffd59X8ljXeun6vvvpqnnV61zV8/PHHtXv3bs2cOdP7J1KKvPk76Cq7Fi9erKysrFzrf/vtN0lSx44di5HjssWb1881Bdny5cs9rnf18m/WrFlRs1tqCFajzPP399cdd9whq9WqkSNH5qhU//DDD1qyZIkqV66sO+64I8d2x44d0759+3IMo9mpUyc1atRIqampGjlyZI5eS3v27HGP4//EE0+Uu/k5vKVfv36qWbOmVq1apS+//NK9PD09XaNHj1ZqaqoGDx6cIziSlZWlffv2ad++fTkKobZt26ply5bat2+fJk+eLIfDIUmy2WwaP3684uPj1b179yvyy7W0efNzuueee2QwGPTDDz/ohx9+yHGcGTNmaP78+QoPDy/3N7dAcXmzPAsODtY999wjSRo9erQOHz7sXnfgwAGNHz9ekvTAAw+U5ClVCN783OA73vzczp49qxEjRigrK0uDBg3SK6+8kmcrZhQO95NA4VEfLj/47ivfqIMDnvHdV3zevIbz5s3Td999J7PZrKlTp7qDaOWZN69fReXNazhw4EDVrl1bBw8e1Lhx43KsmzNnjpYuXapKlSpp0KBBvjk5H/Dm9bv33nslSe+++67WrVuX4zjffvutvvvuO1ksFv3jH/8o4bPyPoPD9a0OlGGpqakaMmSItm3bpoiICHXo0EHHjx/XX3/9JT8/P02fPj3HhPKSNGTIEP355596/PHH9cQTT7iX7969W0OHDtXZs2dVpUoVtW7dWqmpqVq/fr2sVqvuu+8+jR492teneEX5448/9PDDDys9PV0tWrRQ7dq1tWnTJp08eVLNmzfXzJkzFRIS4k5/5MgR9enTR5K0bNmyHL3F9uzZo3/84x9KTExUgwYN1LhxY+3cuVOHDx9WrVq1NGvWLPc8bbg83vycpk+frkmTJkmSmjZtqnr16mnPnj3av3+/goKC9OGHH6p9+/a+PUHgCuTN8iwzM1PDhw/Xb7/9JovFok6dOslms2nTpk1KT0/X3/72N73xxhu+PsVyyZufmyc7d+7Urbfeqlq1auXZOhaXz1uf25QpUzRt2jRJUv/+/fOcx83f39/dUAQF434SKDzqw+UH333lG3VwwDO++4rPG9fQZrOpT58+io+PV/Xq1fPtuXrttdfq1ltvLenT8hlv/g56Mn78eM2YMaNQ9f8rlTev4bZt2/Tggw8qISFB1apVU+vWrXXo0CHFxsbK399fb775pnr37u3zcyxJ3rx+Y8eO1ddffy1JatmypaKiorR3714dOHBAFotF48eP1y233OLbE/QCmsriihAUFKSZM2fqscceU1hYmGJiYnT8+HFdf/31mjNnTq6KeX6aNGmi+fPn6x//+IcCAwO1cuVK7dy5U506ddLUqVOpmBdCp06dNGfOHPXv31/Hjh3TihU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+      "text/plain": [
+       "<Figure size 2000x3000 with 37 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## Set the data and check different visualizations\n",
+    "data = filtered_df_stable\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Set up the figure and axes\n",
+    "fig, axes = plt.subplots(10, 4, figsize=(20, 30))\n",
+    "\n",
+    "# Flatten the axes\n",
+    "axes = axes.flatten()\n",
+    "\n",
+    "# Remove the extra subplots\n",
+    "for i in range(len(data.columns)-1, 40):\n",
+    "    fig.delaxes(axes[i])\n",
+    "\n",
+    "# Create histograms for all score columns\n",
+    "for i, col in enumerate(data.columns[1:]):\n",
+    "    sns.histplot(data[col], ax=axes[i], kde=True)\n",
+    "    axes[i].set_title(col)\n",
+    "\n",
+    "# Improve layout\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(f\"{data_folder}/relevantDrugTargets_scores.pdf\", dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98cb72de-32da-45bb-9ca0-39b0c3449a7c",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "The box plot visualization represents the distribution of scores across all 38 features. Each box represents the interquartile range (IQR), the line within the box is the median, and the whiskers represent the range within 1.5 times the IQR from the first and third quartiles. Observations outside this range are considered outliers and are represented by individual points.\n",
+    "\n",
+    "From the plot, we can observe that:\n",
+    "\n",
+    "    The median of most features is at or near 0, consistent with our observation from the histograms.\n",
+    "    Many features have many outliers, both on the positive and negative sides.\n",
+    "    The range of scores varies between features. Some features have scores mostly concentrated around 0, while others have a      broader range."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "id": "d01167e4-45a1-4cf1-947c-10a10f8fb571",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ACHE</th>\n",
+       "      <th>ADORA2A</th>\n",
+       "      <th>AHR</th>\n",
+       "      <th>SLC25A4</th>\n",
+       "      <th>ANXA2</th>\n",
+       "      <th>ANXA4</th>\n",
+       "      <th>CACNB1</th>\n",
+       "      <th>COL3A1</th>\n",
+       "      <th>CPT2</th>\n",
+       "      <th>DNMT1</th>\n",
+       "      <th>...</th>\n",
+       "      <th>PMEL</th>\n",
+       "      <th>SLC1A5</th>\n",
+       "      <th>VAMP2</th>\n",
+       "      <th>TOP2A</th>\n",
+       "      <th>TYK2</th>\n",
+       "      <th>TYMS</th>\n",
+       "      <th>VWF</th>\n",
+       "      <th>CACNA2D2</th>\n",
+       "      <th>RAMP1</th>\n",
+       "      <th>SCN3B</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>circuit_name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Fanconi anemia pathway: RAD51</th>\n",
+       "      <td>-0.521874</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.563521</td>\n",
+       "      <td>-0.241056</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.0000</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.248767</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.013489</td>\n",
+       "      <td>-0.588002</td>\n",
+       "      <td>0.570760</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>0.535007</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.534459</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fanconi anemia pathway: RAD51C</th>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.0000</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>0.042820</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>0.587580</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>0.477307</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.917447</td>\n",
+       "      <td>-0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fanconi anemia pathway: FANCI</th>\n",
+       "      <td>-0.526704</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>0.401231</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.390779</td>\n",
+       "      <td>-0.0000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.397596</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.787002</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>0.727428</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>0.619025</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.749312</td>\n",
+       "      <td>-0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fanconi anemia pathway: BRCA1</th>\n",
+       "      <td>-0.621120</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.064304</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.047778</td>\n",
+       "      <td>-0.4839</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.022072</td>\n",
+       "      <td>-0.071079</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.136791</td>\n",
+       "      <td>0.055904</td>\n",
+       "      <td>-0.635303</td>\n",
+       "      <td>0.615792</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>0.757284</td>\n",
+       "      <td>-0.447168</td>\n",
+       "      <td>-0.465851</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fanconi anemia pathway: FANCM C19orf40</th>\n",
+       "      <td>-0.644022</td>\n",
+       "      <td>-0.457955</td>\n",
+       "      <td>0.108820</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>0.112910</td>\n",
+       "      <td>-0.0000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.112814</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.389657</td>\n",
+       "      <td>-0.493088</td>\n",
+       "      <td>0.616645</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>0.737936</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.961777</td>\n",
+       "      <td>-0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 38 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            ACHE   ADORA2A       AHR  \\\n",
+       "circuit_name                                                           \n",
+       "Fanconi anemia pathway: RAD51          -0.521874 -0.000000 -0.000000   \n",
+       "Fanconi anemia pathway: RAD51C         -0.000000 -0.000000 -0.000000   \n",
+       "Fanconi anemia pathway: FANCI          -0.526704 -0.000000  0.401231   \n",
+       "Fanconi anemia pathway: BRCA1          -0.621120 -0.000000 -0.064304   \n",
+       "Fanconi anemia pathway: FANCM C19orf40 -0.644022 -0.457955  0.108820   \n",
+       "\n",
+       "                                         SLC25A4     ANXA2     ANXA4  CACNB1  \\\n",
+       "circuit_name                                                                   \n",
+       "Fanconi anemia pathway: RAD51          -0.563521 -0.241056 -0.000000 -0.0000   \n",
+       "Fanconi anemia pathway: RAD51C         -0.000000 -0.000000 -0.000000 -0.0000   \n",
+       "Fanconi anemia pathway: FANCI          -0.000000  0.000000  0.390779 -0.0000   \n",
+       "Fanconi anemia pathway: BRCA1          -0.000000 -0.000000 -0.047778 -0.4839   \n",
+       "Fanconi anemia pathway: FANCM C19orf40 -0.000000 -0.000000  0.112910 -0.0000   \n",
+       "\n",
+       "                                        COL3A1      CPT2     DNMT1  ...  \\\n",
+       "circuit_name                                                        ...   \n",
+       "Fanconi anemia pathway: RAD51             -0.0 -0.000000 -0.248767  ...   \n",
+       "Fanconi anemia pathway: RAD51C            -0.0 -0.000000 -0.000000  ...   \n",
+       "Fanconi anemia pathway: FANCI              0.0  0.397596  0.000000  ...   \n",
+       "Fanconi anemia pathway: BRCA1             -0.0 -0.022072 -0.071079  ...   \n",
+       "Fanconi anemia pathway: FANCM C19orf40     0.0  0.112814 -0.000000  ...   \n",
+       "\n",
+       "                                            PMEL    SLC1A5     VAMP2  \\\n",
+       "circuit_name                                                           \n",
+       "Fanconi anemia pathway: RAD51          -0.000000 -0.013489 -0.588002   \n",
+       "Fanconi anemia pathway: RAD51C         -0.000000  0.042820 -0.000000   \n",
+       "Fanconi anemia pathway: FANCI           0.000000  0.787002 -0.000000   \n",
+       "Fanconi anemia pathway: BRCA1          -0.136791  0.055904 -0.635303   \n",
+       "Fanconi anemia pathway: FANCM C19orf40  0.000000  0.389657 -0.493088   \n",
+       "\n",
+       "                                           TOP2A  TYK2      TYMS       VWF  \\\n",
+       "circuit_name                                                                 \n",
+       "Fanconi anemia pathway: RAD51           0.570760  -0.0  0.535007 -0.000000   \n",
+       "Fanconi anemia pathway: RAD51C          0.587580  -0.0  0.477307 -0.000000   \n",
+       "Fanconi anemia pathway: FANCI           0.727428  -0.0  0.619025 -0.000000   \n",
+       "Fanconi anemia pathway: BRCA1           0.615792  -0.0  0.757284 -0.447168   \n",
+       "Fanconi anemia pathway: FANCM C19orf40  0.616645  -0.0  0.737936 -0.000000   \n",
+       "\n",
+       "                                        CACNA2D2     RAMP1     SCN3B  \n",
+       "circuit_name                                                          \n",
+       "Fanconi anemia pathway: RAD51          -0.000000 -1.000000 -0.534459  \n",
+       "Fanconi anemia pathway: RAD51C         -0.000000 -0.917447 -0.000000  \n",
+       "Fanconi anemia pathway: FANCI           0.000000 -0.749312 -0.000000  \n",
+       "Fanconi anemia pathway: BRCA1          -0.465851 -1.000000 -0.000000  \n",
+       "Fanconi anemia pathway: FANCM C19orf40 -0.000000 -0.961777 -0.000000  \n",
+       "\n",
+       "[5 rows x 38 columns]"
+      ]
+     },
+     "execution_count": 158,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "id": "500e3622-8859-4370-9d85-665a8560c416",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 2000x1200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Melt the DataFrame to make it suitable for boxplots\n",
+    "data.reset_index(inplace=True) ## Reset the index back to a column\n",
+    "melted_data = data.melt(id_vars = \"circuit_name\")\n",
+    "\n",
+    "# Compute median of each column\n",
+    "medians = melted_data.groupby(\"variable\")[\"value\"].median().sort_values(ascending=False)\n",
+    "\n",
+    "# Order the data based on medians\n",
+    "melted_data[\"variable\"] = pd.Categorical(melted_data[\"variable\"], categories=medians.index, ordered=True)\n",
+    "\n",
+    "# Create the boxplots\n",
+    "plt.rcParams['font.family'] = 'DejaVu Sans'\n",
+    "plt.figure(figsize=(20, 12), facecolor='white')\n",
+    "sns.set_style(\"whitegrid\")  # set Seaborn style to white grid\n",
+    "sns.boxplot(x=\"variable\", y=\"value\", data = melted_data)\n",
+    "plt.xticks(rotation=40, ha = \"right\")\n",
+    "plt.title(\"Boxplots of SHAP-Scores\")\n",
+    "plt.ylabel('SHAP Scores')\n",
+    "plt.xlabel('Drug-target')\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(f\"{data_folder}/relevant_drugTargetscores_boxplots.pdf\", dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "id": "c92a32bd-4e63-43ef-89bf-e9d29f4eab0f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                        drug drug_action    KDT  KDT_name Drug_effect  \\\n",
+      "10               Neostigmine   inhibitor     43      ACHE   Inhibitor   \n",
+      "11            Pyridostigmine   inhibitor     43      ACHE   Inhibitor   \n",
+      "12               Edrophonium   inhibitor     43      ACHE   Inhibitor   \n",
+      "13                Demecarium   inhibitor     43      ACHE   Inhibitor   \n",
+      "14    Gallamine triethiodide   inhibitor     43      ACHE   Inhibitor   \n",
+      "...                      ...         ...    ...       ...         ...   \n",
+      "2570         Lonoctocog alfa      binder   7450       VWF      Ligand   \n",
+      "2571         Susoctocog alfa     binding   7450       VWF      Ligand   \n",
+      "2617              Isradipine   inhibitor   9254  CACNA2D2   Inhibitor   \n",
+      "2648             Pramlintide     agonist  10267     RAMP1   Activator   \n",
+      "2701              Zonisamide   inhibitor  55800     SCN3B   Inhibitor   \n",
+      "\n",
+      "                       drugKDT  \n",
+      "10               Neostigmine43  \n",
+      "11            Pyridostigmine43  \n",
+      "12               Edrophonium43  \n",
+      "13                Demecarium43  \n",
+      "14    Gallamine triethiodide43  \n",
+      "...                        ...  \n",
+      "2570       Lonoctocog alfa7450  \n",
+      "2571       Susoctocog alfa7450  \n",
+      "2617            Isradipine9254  \n",
+      "2648          Pramlintide10267  \n",
+      "2701           Zonisamide55800  \n",
+      "\n",
+      "[102 rows x 6 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Now lets create a  heatmap with the drugs\n",
+    "\n",
+    "# Load the new data\n",
+    "drugs = pd.read_csv(\"/home/m3m/INFO_PROYECTO/drexml_paper_paquete/data/drugbank-v050110_alldrugbyaction.tsv\", sep='\\t')\n",
+    "\n",
+    "# List of genes in the initial dataset\n",
+    "genes = data.columns[1:].tolist()\n",
+    "\n",
+    "# Filter the drugs dataset to keep only those rows where the gene is in our list\n",
+    "drugs_filtered = drugs[drugs['KDT_name'].isin(genes)]\n",
+    "\n",
+    "# Display the first few rows of the filtered dataset\n",
+    "print(drugs_filtered)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "id": "006c47c0-2ed6-43a2-9540-72be3f2db978",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculate the mean absolute score for each gene\n",
+    "mean_scores = data.drop(columns=\"circuit_name\").abs().mean()\n",
+    "\n",
+    "# Copy the DataFrame\n",
+    "drugs_filtered_scores = drugs_filtered.copy()\n",
+    "\n",
+    "# Map the mean scores to the drugs that target each gene\n",
+    "drugs_filtered_scores['mean_score'] = drugs_filtered_scores['KDT_name'].map(mean_scores)\n",
+    "\n",
+    "# Pivot the table to create a matrix of drugs and their mean scores for each gene\n",
+    "drug_gene_scores = drugs_filtered_scores.pivot_table(index='drug', columns='KDT_name', values='mean_score')\n",
+    "\n",
+    "# Replace NaN values with 0\n",
+    "drug_gene_scores = drug_gene_scores.fillna(0)\n",
+    "\n",
+    "# Display the first few rows of the dataset\n",
+    "drug_gene_scores.head()\n",
+    "\n",
+    "drug_gene_scores.to_csv(f\"{data_folder}/drug_KDT_scores.tsv\", sep='\\t', index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "id": "ec34c062-7c38-49dc-9e4c-aaa7d69b2a62",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x1200 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a heatmap of the drug-gene scores\n",
+    "sns.set(font_scale= 0.5) # Adjust the font size if necessary\n",
+    "\n",
+    "plt.figure(figsize=(16, 12))\n",
+    "ax = sns.heatmap(drug_gene_scores, cmap='Purples', linecolor='black', linewidths=0.5, cbar_kws={'label': 'Mean Absolute Score'})\n",
+    "\n",
+    "# Set the yticks to each row and the yticklabels to the index of the DataFrame\n",
+    "ax.set_yticks([x + 0.5 for x in range(drug_gene_scores.shape[0])])  # shift the yticks by 0.5\n",
+    "ax.set_yticklabels(drug_gene_scores.index, size='small')\n",
+    "\n",
+    "# Ensure that the ticks on the y-axis are visible\n",
+    "ax.yaxis.tick_left()\n",
+    "\n",
+    "plt.title(\"Heatmap of Mean Absolute relevance score of Drug-Targets annotated with their targeting drug\", size)\n",
+    "plt.xticks(rotation=90, ha = \"center\")\n",
+    "plt.savefig(f\"{data_folder}/relevant_drugTargets_withdrugs.pdf\", dpi=300)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "id": "8eeec3f5-8bb1-42d7-b030-9a7f34ba0a9d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fanconi anemia pathway: RAD51</th>\n",
+       "      <th>Fanconi anemia pathway: RAD51C</th>\n",
+       "      <th>Fanconi anemia pathway: FANCI</th>\n",
+       "      <th>Fanconi anemia pathway: BRCA1</th>\n",
+       "      <th>Fanconi anemia pathway: FANCM C19orf40</th>\n",
+       "      <th>Fanconi anemia pathway: FANCM STRA13</th>\n",
+       "      <th>Cell cycle: SMC1B SMC3 STAG1 RAD21</th>\n",
+       "      <th>Oocyte meiosis: REC8*</th>\n",
+       "      <th>Progesterone-mediated oocyte maturation: ANAPC10</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Neostigmine</th>\n",
+       "      <td>0.521874</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.526704</td>\n",
+       "      <td>0.62112</td>\n",
+       "      <td>0.644022</td>\n",
+       "      <td>0.690006</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.542976</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pyridostigmine</th>\n",
+       "      <td>0.521874</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.526704</td>\n",
+       "      <td>0.62112</td>\n",
+       "      <td>0.644022</td>\n",
+       "      <td>0.690006</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.542976</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Edrophonium</th>\n",
+       "      <td>0.521874</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.526704</td>\n",
+       "      <td>0.62112</td>\n",
+       "      <td>0.644022</td>\n",
+       "      <td>0.690006</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.542976</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Demecarium</th>\n",
+       "      <td>0.521874</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.526704</td>\n",
+       "      <td>0.62112</td>\n",
+       "      <td>0.644022</td>\n",
+       "      <td>0.690006</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.542976</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gallamine triethiodide</th>\n",
+       "      <td>0.521874</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.526704</td>\n",
+       "      <td>0.62112</td>\n",
+       "      <td>0.644022</td>\n",
+       "      <td>0.690006</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.542976</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        Fanconi anemia pathway: RAD51  \\\n",
+       "Neostigmine                                  0.521874   \n",
+       "Pyridostigmine                               0.521874   \n",
+       "Edrophonium                                  0.521874   \n",
+       "Demecarium                                   0.521874   \n",
+       "Gallamine triethiodide                       0.521874   \n",
+       "\n",
+       "                        Fanconi anemia pathway: RAD51C  \\\n",
+       "Neostigmine                                        0.0   \n",
+       "Pyridostigmine                                     0.0   \n",
+       "Edrophonium                                        0.0   \n",
+       "Demecarium                                         0.0   \n",
+       "Gallamine triethiodide                             0.0   \n",
+       "\n",
+       "                        Fanconi anemia pathway: FANCI  \\\n",
+       "Neostigmine                                  0.526704   \n",
+       "Pyridostigmine                               0.526704   \n",
+       "Edrophonium                                  0.526704   \n",
+       "Demecarium                                   0.526704   \n",
+       "Gallamine triethiodide                       0.526704   \n",
+       "\n",
+       "                        Fanconi anemia pathway: BRCA1  \\\n",
+       "Neostigmine                                   0.62112   \n",
+       "Pyridostigmine                                0.62112   \n",
+       "Edrophonium                                   0.62112   \n",
+       "Demecarium                                    0.62112   \n",
+       "Gallamine triethiodide                        0.62112   \n",
+       "\n",
+       "                        Fanconi anemia pathway: FANCM C19orf40  \\\n",
+       "Neostigmine                                           0.644022   \n",
+       "Pyridostigmine                                        0.644022   \n",
+       "Edrophonium                                           0.644022   \n",
+       "Demecarium                                            0.644022   \n",
+       "Gallamine triethiodide                                0.644022   \n",
+       "\n",
+       "                        Fanconi anemia pathway: FANCM STRA13  \\\n",
+       "Neostigmine                                         0.690006   \n",
+       "Pyridostigmine                                      0.690006   \n",
+       "Edrophonium                                         0.690006   \n",
+       "Demecarium                                          0.690006   \n",
+       "Gallamine triethiodide                              0.690006   \n",
+       "\n",
+       "                        Cell cycle: SMC1B SMC3 STAG1 RAD21  \\\n",
+       "Neostigmine                                            0.0   \n",
+       "Pyridostigmine                                         0.0   \n",
+       "Edrophonium                                            0.0   \n",
+       "Demecarium                                             0.0   \n",
+       "Gallamine triethiodide                                 0.0   \n",
+       "\n",
+       "                        Oocyte meiosis: REC8*  \\\n",
+       "Neostigmine                          0.542976   \n",
+       "Pyridostigmine                       0.542976   \n",
+       "Edrophonium                          0.542976   \n",
+       "Demecarium                           0.542976   \n",
+       "Gallamine triethiodide               0.542976   \n",
+       "\n",
+       "                        Progesterone-mediated oocyte maturation: ANAPC10  \n",
+       "Neostigmine                                                          0.0  \n",
+       "Pyridostigmine                                                       0.0  \n",
+       "Edrophonium                                                          0.0  \n",
+       "Demecarium                                                           0.0  \n",
+       "Gallamine triethiodide                                               0.0  "
+      ]
+     },
+     "execution_count": 162,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Filter the drugs dataset to keep only those rows where the gene is in our list\n",
+    "genes = data.columns[1:].tolist()\n",
+    "drugs_filtered = drugs[drugs['KDT_name'].isin(genes)]\n",
+    "\n",
+    "# Create a DataFrame to hold the mean scores for each drug-circuit pair\n",
+    "drug_circuit_scores = pd.DataFrame(index=drugs_filtered['drug'].unique(), columns=data['circuit_name'].unique())\n",
+    "\n",
+    "# Get the absolute scores for each circuit and gene\n",
+    "abs_scores = data.set_index(\"circuit_name\").abs()\n",
+    "\n",
+    "# Create a mapping from drug to genes\n",
+    "drug_genes = drugs_filtered.groupby('drug')['KDT_name'].apply(list).to_dict()\n",
+    "\n",
+    "# Calculate the mean scores for each drug-circuit pair\n",
+    "for drug, genes in drug_genes.items():\n",
+    "    for circuit in drug_circuit_scores.columns:\n",
+    "        # Get the scores for the genes that are in both the drug's targets and the circuit\n",
+    "        scores = abs_scores.loc[circuit, genes]\n",
+    "        # Calculate the mean score\n",
+    "        drug_circuit_scores.loc[drug, circuit] = scores.mean()\n",
+    "\n",
+    "# Convert the scores to floats\n",
+    "drug_circuit_scores = drug_circuit_scores.astype(float)\n",
+    "\n",
+    "drug_circuit_scores.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "id": "ef8d33f8-c96c-4f64-b5fb-64b126c7a9a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                        drug drug_action    KDT  KDT_name Drug_effect  \\\n",
+      "10               Neostigmine   inhibitor     43      ACHE   Inhibitor   \n",
+      "11            Pyridostigmine   inhibitor     43      ACHE   Inhibitor   \n",
+      "12               Edrophonium   inhibitor     43      ACHE   Inhibitor   \n",
+      "13                Demecarium   inhibitor     43      ACHE   Inhibitor   \n",
+      "14    Gallamine triethiodide   inhibitor     43      ACHE   Inhibitor   \n",
+      "...                      ...         ...    ...       ...         ...   \n",
+      "2570         Lonoctocog alfa      binder   7450       VWF      Ligand   \n",
+      "2571         Susoctocog alfa     binding   7450       VWF      Ligand   \n",
+      "2617              Isradipine   inhibitor   9254  CACNA2D2   Inhibitor   \n",
+      "2648             Pramlintide     agonist  10267     RAMP1   Activator   \n",
+      "2701              Zonisamide   inhibitor  55800     SCN3B   Inhibitor   \n",
+      "\n",
+      "                       drugKDT  \n",
+      "10               Neostigmine43  \n",
+      "11            Pyridostigmine43  \n",
+      "12               Edrophonium43  \n",
+      "13                Demecarium43  \n",
+      "14    Gallamine triethiodide43  \n",
+      "...                        ...  \n",
+      "2570       Lonoctocog alfa7450  \n",
+      "2571       Susoctocog alfa7450  \n",
+      "2617            Isradipine9254  \n",
+      "2648          Pramlintide10267  \n",
+      "2701           Zonisamide55800  \n",
+      "\n",
+      "[102 rows x 6 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(drugs_filtered)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "id": "4a82a5f3-c77a-44e7-8a65-f2e662bf91bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x1200 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create the heatmap\n",
+    "plt.figure(figsize=(16, 12))\n",
+    "sns.heatmap(drug_circuit_scores, cmap='Blues', linewidths=0.5, linecolor='gray', cbar_kws={'label': 'Mean Absolute Score'})\n",
+    "plt.title(\"Heatmap of Mean Absolute Scores for Drug-Circuit Interactions\")\n",
+    "plt.xticks(rotation=30, ha = \"right\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a4e3b77b-892d-40e6-a026-cfd0a7008e99",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/drexml/heatmap_shap_scores.py b/drexml/heatmap_shap_scores.py
new file mode 100644
index 0000000..1f8a7a5
--- /dev/null
+++ b/drexml/heatmap_shap_scores.py
@@ -0,0 +1,64 @@
+# -*- coding: utf-8 -*-
+"""
+SHAP scores analysis on Heatmap for DREXML.
+"""
+
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+def create_heatmap_KDTsCir_annot(data_folder, assets_folder, filtered_file_name):
+    """
+    This function reads the filtered shap_relevant_table data generated from the process_data() function  and the annotations 
+    of most common drug-effect over the drug-targets. Then, it creates a heatmap with the drug-targets (annotated with the most
+    common drug effect on top) as columns and the circuits as rows, filled  with the SHAP scores obtained. 
+    The heatmap is saved to a .pdf file on the same data_folder.
+
+    Args:
+    data_folder (str): The path to the data folder.
+    assets_folder (str): The path to the assets folder.
+    filtered_file_name (str): The name of the shap scores filtered file.
+
+    Returns:
+    None. The heatmap is saved as a .pdf file.
+    """
+    # Load the previous filtered table
+    filtered_df_stable = pd.read_csv(f"{data_folder}/{filtered_file_name}", sep='\t', index_col=0)
+
+    # Load annotations of the most common drug-effects over the drug-targets
+    annots = pd.read_csv(f"{assets_folder}/drugbank-v050110_mostdrugeffects_KDTs.tsv", sep='\t', index_col=0)
+
+    # Subset the relevant drug-targets
+    annots = annots.loc[annots.index.intersection(filtered_df_stable.columns),]
+
+    drug_action_colors = {
+        "Inhibitor": "magenta",
+        "Activator": "cyan",
+        "Ligand": "yellow",
+        "other": "grey",
+    }
+    drug_action_colors_list = [drug_action_colors[action] for action in annots["drug_action"]]
+
+    sns.set(font_scale= 1.5) # Adjust the font size if necessary
+
+    # Create the clustermap
+    cluster_map = sns.clustermap(filtered_df_stable, cmap="RdBu_r", col_colors=[drug_action_colors_list], linewidths=0.5, cbar_kws={'label': 'SHAP Value'}, figsize=(20,20))
+
+    # Add titles to the x and y axes
+    cluster_map.ax_heatmap.set_xlabel('Drug-target')
+    cluster_map.ax_heatmap.set_ylabel('Circuit name')
+
+    # Add a title to the color legend
+    legend_elements = [
+        plt.Line2D([0], [0], marker='o', color='w', markerfacecolor=color, label=action, markersize=15)
+        for action, color in drug_action_colors.items()
+    ]
+    legend = plt.legend(handles=legend_elements, title="Drug Action", loc='upper left', bbox_to_anchor=(15, 1))
+    legend.get_frame().set_facecolor('white')
+
+    # Add a title to the entire figure
+    plt.suptitle('Heatmap of circuits and genes', fontsize=30, y=1.05)
+
+    # Save the plot
+    plt.savefig(f"{data_folder}/heatmap_circuits_KDT_drugeff.pdf", dpi=300)
+    plt.show()
diff --git a/drexml/heatmap_top10KDT_drugs.py b/drexml/heatmap_top10KDT_drugs.py
new file mode 100644
index 0000000..f7c09aa
--- /dev/null
+++ b/drexml/heatmap_top10KDT_drugs.py
@@ -0,0 +1,64 @@
+# -*- coding: utf-8 -*-
+"""
+Analysis of top 10 drug-targets by their drugs on a Heatmap for DREXML.
+"""
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+
+
+def create_heatmap_drugsCircuit_top10KDTs(KDTs_path, drugs_path, data_path):
+    """
+    This function reads the filtered matrix of shap scores (columns=drug-targets, rows = disease-circuits) and the drug-target-effects data, 
+    filters the top 10 best-scored drug-targets from SHAP and creates a heatmap with the drugs from the top 10 scored targets and the circuits.
+    
+    Args:
+    KDTs_path (str): The path to the shap_scores data file.
+    drugs_path (str): The path to the drug-target interactions file.
+    data_path (str): The path to the data folder where all results from the DREXML model are.
+    
+    
+    Returns:
+    A heatmap is displayed and saved in the data_path.
+    """
+
+    # Read the genes data
+    df = pd.read_csv(KDTs_path, sep='\t', index_col="circuit_name")
+
+    # Calculate the mean of absolute values for each gene
+    mean_scores = df.abs().mean()
+
+    # Get the top 10 genes by mean score
+    top_10_genes = mean_scores.nlargest(10)
+
+    # Read the drugs data
+    df_drugs = pd.read_csv(drugs_path, sep='\t', skiprows=1, names=["Drug", "Action", "KDT", "Gene", "Drug_effect", "drugKDT"])
+
+    # Filter the drugs that target the top 10 genes
+    df_drugs_top_10_genes = df_drugs[df_drugs['Gene'].isin(top_10_genes.index)]
+
+    # Create a new dataframe where each row corresponds to a drug and a circuit, and the value is the mean score of the top gene targeted by the drug in that circuit
+    df_heatmap_10 = pd.DataFrame(index=df.index.unique(), columns=df_drugs_top_10_genes['Drug'].unique())
+
+    # Populate the dataframe with mean scores
+    for circuit in df_heatmap_10.index:
+        for drug in df_heatmap_10.columns:
+            # Get the genes targeted by the drug
+            genes = df_drugs_top_10_genes.loc[df_drugs_top_10_genes['Drug'] == drug, 'Gene'].values
+            
+            # Calculate the mean score of the genes in the circuit
+            df_heatmap_10.loc[circuit, drug] = df.loc[circuit, genes].abs().mean()
+
+    # Convert the scores to floats
+    df_heatmap_10 = df_heatmap_10.astype(float)
+
+    # Plot a heatmap
+    plt.figure(figsize=(12, 10))
+    sns.heatmap(df_heatmap_10, cmap="Blues", center=0)
+    plt.title('Mean scores of top 10 genes targeted by each drug in each circuit')
+    plt.xlabel('Drug')
+    plt.ylabel('Circuit')
+    plt.savefig(f"{data_path}/heatmap_top10KDTdrugs_circuit.pdf", dpi=300)
+    plt.show()
\ No newline at end of file
diff --git a/drexml/histograms_shap_scores.py b/drexml/histograms_shap_scores.py
new file mode 100644
index 0000000..36d219a
--- /dev/null
+++ b/drexml/histograms_shap_scores.py
@@ -0,0 +1,43 @@
+# -*- coding: utf-8 -*-
+"""
+SHAP scores analysis on histograms for DREXML.
+"""
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+def create_histograms(data_folder, filtered_file_name):
+    """
+    This function reads the shap scores filtered data, creates histograms from the SHAP scores of each drug-target, 
+    and saves the histograms to a composed .pdf file.
+
+    Args:
+    data_folder (str): The path to the data folder.
+    filtered_file_name (str): The name of the  shap scores filtered file.
+
+    Returns:
+    None. The histograms are saved as a .pdf file.
+    """
+    # Load the previous filtered table
+    data = pd.read_csv(f"{data_folder}/{filtered_file_name}", sep='\t', index_col=0)
+
+    # Set up the figure and axes
+    fig, axes = plt.subplots(10, 4, figsize=(20, 30))
+
+    # Flatten the axes
+    axes = axes.flatten()
+
+    # Remove the extra subplots
+    for i in range(len(data.columns)-1, 40):
+        fig.delaxes(axes[i])
+
+    # Create histograms for all score columns
+    for i, col in enumerate(data.columns[1:]):
+        sns.histplot(data[col], ax=axes[i], kde=True)
+        axes[i].set_title(col)
+
+    # Improve layout
+    plt.tight_layout()
+    plt.savefig(f"{data_folder}/relevantDrugTargets_scores.pdf", dpi=300)
+    plt.show()
\ No newline at end of file
diff --git a/drexml/resources/drugbank-v050110_alldrugbyaction.tsv b/drexml/resources/drugbank-v050110_alldrugbyaction.tsv
new file mode 100644
index 0000000..18892d8
--- /dev/null
+++ b/drexml/resources/drugbank-v050110_alldrugbyaction.tsv
@@ -0,0 +1,2771 @@
+drug	drug_action	KDT	KDT_name	Drug_effect_simplified
+Vigabatrin	inhibitor	18	ABAT	Inhibitor
+Probucol	inhibitor	19	ABCA1	Inhibitor
+Ponatinib	inhibitor	25	ABL1	Inhibitor
+Regorafenib	inhibitor	25	ABL1	Inhibitor
+Nilotinib	inhibitor	25	ABL1	Inhibitor
+Imatinib	inhibitor	25	ABL1	Inhibitor
+Bosutinib	inhibitor	25	ABL1	Inhibitor
+Asciminib	inhibitor|allosteric modulator	25	ABL1	Inhibitor
+Dasatinib	multitarget	25	ABL1	Inhibitor
+Sulfasalazine	inhibitor	38	ACAT1	Inhibitor
+Neostigmine	inhibitor	43	ACHE	Inhibitor
+Pyridostigmine	inhibitor	43	ACHE	Inhibitor
+Edrophonium	inhibitor	43	ACHE	Inhibitor
+Demecarium	inhibitor	43	ACHE	Inhibitor
+Gallamine triethiodide	inhibitor	43	ACHE	Inhibitor
+Pralidoxime	activator	43	ACHE	Activator
+Rivastigmine	inhibitor	43	ACHE	Inhibitor
+Minaprine	inhibitor	43	ACHE	Inhibitor
+Donepezil	inhibitor	43	ACHE	Inhibitor
+Ambenonium	inhibitor	43	ACHE	Inhibitor
+Physostigmine	inhibitor	43	ACHE	Inhibitor
+Galantamine	inhibitor	43	ACHE	Inhibitor
+Bempedoic acid	inhibitor	47	ACLY	Inhibitor
+Sipuleucel-T	other	55	ACP3	other
+Human calcitonin	incorporation into and destabilization	87	ACTN1	other
+Pentostatin	inhibitor	100	ADA	Inhibitor
+Dipyridamole	inhibitor	100	ADA	Inhibitor
+Fomepizole	inhibitor	124	ADH1A	Inhibitor
+Fomepizole	inhibitor	125	ADH1B	Inhibitor
+Fomepizole	inhibitor	126	ADH1C	Inhibitor
+Adenosine	agonist	134	ADORA1	Activator
+Theophylline	antagonist	134	ADORA1	Inhibitor
+Aminophylline	antagonist	134	ADORA1	Inhibitor
+Tramadol	agonist	134	ADORA1	Activator
+Oxtriphylline	antagonist	134	ADORA1	Inhibitor
+Caffeine	antagonist	134	ADORA1	Inhibitor
+Dyphylline	antagonist	134	ADORA1	Inhibitor
+Pentoxifylline	agonist	135	ADORA2A	Activator
+Regadenoson	agonist	135	ADORA2A	Activator
+Istradefylline	antagonist	135	ADORA2A	Inhibitor
+Dyphylline	antagonist	135	ADORA2A	Inhibitor
+Caffeine	antagonist	135	ADORA2A	Inhibitor
+Theophylline	antagonist	135	ADORA2A	Inhibitor
+Oxtriphylline	antagonist	135	ADORA2A	Inhibitor
+Adenosine	agonist	135	ADORA2A	Activator
+Adenosine	agonist	136	ADORA2B	Activator
+Caffeine	antagonist	136	ADORA2B	Inhibitor
+Enprofylline	antagonist	136	ADORA2B	Inhibitor
+Theophylline	antagonist	136	ADORA2B	Inhibitor
+Aminophylline	antagonist	140	ADORA3	Inhibitor
+Adenosine	agonist	140	ADORA3	Activator
+Caffeine	antagonist	140	ADORA3	Inhibitor
+Olaparib	inhibitor	142	PARP1	Inhibitor
+Niraparib	inhibitor	142	PARP1	Inhibitor
+Rucaparib	inhibitor	142	PARP1	Inhibitor
+Talazoparib	inhibitor	142	PARP1	Inhibitor
+Silodosin	antagonist	146	ADRA1D	Inhibitor
+Dronedarone	antagonist	146	ADRA1D	Inhibitor
+Alfuzosin	antagonist	146	ADRA1D	Inhibitor
+Norepinephrine	agonist	146	ADRA1D	Activator
+Terazosin	antagonist	146	ADRA1D	Inhibitor
+Dapiprazole	antagonist	146	ADRA1D	Inhibitor
+Racepinephrine	antagonist	146	ADRA1D	Inhibitor
+Doxazosin	antagonist	146	ADRA1D	Inhibitor
+Droxidopa	agonist	146	ADRA1D	Activator
+Phenylephrine	agonist	146	ADRA1D	Activator
+Prazosin	antagonist	146	ADRA1D	Inhibitor
+Ergotamine	partial agonist	146	ADRA1D	Modulator
+Ergotamine	partial agonist	147	ADRA1B	Modulator
+Brexpiprazole	antagonist	147	ADRA1B	Inhibitor
+Midodrine	agonist	147	ADRA1B	Activator
+Chlorpromazine	antagonist	147	ADRA1B	Inhibitor
+Terazosin	antagonist	147	ADRA1B	Inhibitor
+Prazosin	antagonist	147	ADRA1B	Inhibitor
+Alfuzosin	antagonist	147	ADRA1B	Inhibitor
+Silodosin	antagonist	147	ADRA1B	Inhibitor
+Dapiprazole	antagonist	147	ADRA1B	Inhibitor
+Racepinephrine	agonist	147	ADRA1B	Activator
+Droxidopa	agonist	147	ADRA1B	Activator
+Dronedarone	antagonist	147	ADRA1B	Inhibitor
+Phenylephrine	agonist	147	ADRA1B	Activator
+Epinephrine	agonist	147	ADRA1B	Activator
+Norepinephrine	agonist	147	ADRA1B	Activator
+Terazosin	antagonist	148	ADRA1A	Inhibitor
+Racepinephrine	agonist	148	ADRA1A	Activator
+Benzphetamine	agonist	148	ADRA1A	Activator
+Chlorpromazine	antagonist	148	ADRA1A	Inhibitor
+Doxazosin	antagonist	148	ADRA1A	Inhibitor
+Oxymetazoline	partial agonist	148	ADRA1A	Modulator
+Metaraminol	agonist	148	ADRA1A	Activator
+Isometheptene	agonist	148	ADRA1A	Activator
+Xylometazoline	agonist|partial agonist	148	ADRA1A	Activator
+Epinephrine	agonist	148	ADRA1A	Activator
+Phenoxybenzamine	antagonist	148	ADRA1A	Inhibitor
+Dronedarone	antagonist	148	ADRA1A	Inhibitor
+Tolazoline	antagonist	148	ADRA1A	Inhibitor
+Prazosin	antagonist	148	ADRA1A	Inhibitor
+Silodosin	antagonist	148	ADRA1A	Inhibitor
+Phenylephrine	agonist	148	ADRA1A	Activator
+Trifluoperazine	antagonist	148	ADRA1A	Inhibitor
+Dipivefrin	agonist	148	ADRA1A	Activator
+Alfuzosin	antagonist	148	ADRA1A	Inhibitor
+Nicergoline	antagonist	148	ADRA1A	Inhibitor
+Methoxamine	agonist	148	ADRA1A	Activator
+Naphazoline	agonist	148	ADRA1A	Activator
+Ergotamine	partial agonist	148	ADRA1A	Modulator
+Apraclonidine	agonist	148	ADRA1A	Activator
+Midodrine	agonist	148	ADRA1A	Activator
+Carvedilol	antagonist|potentiator	148	ADRA1A	Activator
+Phentolamine	antagonist	148	ADRA1A	Inhibitor
+Propiverine	antagonist	148	ADRA1A	Inhibitor
+Phendimetrazine	agonist	148	ADRA1A	Activator
+Dapiprazole	antagonist	148	ADRA1A	Inhibitor
+Droperidol	antagonist	148	ADRA1A	Inhibitor
+Pseudoephedrine	agonist	148	ADRA1A	Activator
+Ergometrine	agonist	148	ADRA1A	Activator
+Droxidopa	agonist	148	ADRA1A	Activator
+Tamsulosin	antagonist	148	ADRA1A	Inhibitor
+Ephedrine	agonist	148	ADRA1A	Activator
+Norepinephrine	agonist	148	ADRA1A	Activator
+Phentolamine	antagonist	150	ADRA2A	Inhibitor
+Pseudoephedrine	agonist	150	ADRA2A	Activator
+Epinephrine	agonist	150	ADRA2A	Activator
+Apraclonidine	agonist	150	ADRA2A	Activator
+Dexmedetomidine	agonist	150	ADRA2A	Activator
+Clonidine	agonist	150	ADRA2A	Activator
+DL-Methylephedrine	agonist	150	ADRA2A	Activator
+Lofexidine	agonist	150	ADRA2A	Activator
+Yohimbine	antagonist	150	ADRA2A	Inhibitor
+Periciazine	antagonist	150	ADRA2A	Inhibitor
+Mirtazapine	antagonist	150	ADRA2A	Inhibitor
+Dronedarone	antagonist	150	ADRA2A	Inhibitor
+Guanabenz	agonist	150	ADRA2A	Activator
+Dipivefrin	agonist	150	ADRA2A	Activator
+Naphazoline	agonist	150	ADRA2A	Activator
+Brimonidine	agonist	150	ADRA2A	Activator
+Norepinephrine	agonist	150	ADRA2A	Activator
+Xylometazoline	agonist	150	ADRA2A	Activator
+Guanfacine	agonist	150	ADRA2A	Activator
+Methyldopa	agonist	150	ADRA2A	Activator
+Droxidopa	agonist	150	ADRA2A	Activator
+Benzphetamine	agonist	150	ADRA2A	Activator
+Racepinephrine	agonist	150	ADRA2A	Activator
+Oxymetazoline	agonist|partial agonist	150	ADRA2A	Activator
+Phenoxybenzamine	antagonist	150	ADRA2A	Inhibitor
+Moxonidine	agonist	150	ADRA2A	Activator
+Clonidine	agonist	151	ADRA2B	Activator
+Racepinephrine	agonist	151	ADRA2B	Activator
+Droxidopa	agonist	151	ADRA2B	Activator
+Norepinephrine	agonist	151	ADRA2B	Activator
+Brimonidine	agonist	151	ADRA2B	Activator
+Xylometazoline	agonist	151	ADRA2B	Activator
+Dronedarone	antagonist	151	ADRA2B	Inhibitor
+Yohimbine	antagonist	151	ADRA2B	Inhibitor
+Oxymetazoline	agonist	151	ADRA2B	Activator
+Etomidate	agonist	151	ADRA2B	Activator
+Epinephrine	agonist	151	ADRA2B	Activator
+Norepinephrine	agonist	152	ADRA2C	Activator
+Brimonidine	agonist	152	ADRA2C	Activator
+Dronedarone	antagonist	152	ADRA2C	Inhibitor
+Yohimbine	antagonist	152	ADRA2C	Inhibitor
+Droxidopa	agonist	152	ADRA2C	Activator
+Brexpiprazole	antagonist	152	ADRA2C	Inhibitor
+Clonidine	agonist	152	ADRA2C	Activator
+Racepinephrine	agonist	152	ADRA2C	Activator
+Pindolol	partial agonist	153	ADRB1	Modulator
+Propranolol	antagonist	153	ADRB1	Inhibitor
+Carteolol	partial agonist	153	ADRB1	Modulator
+DL-Methylephedrine	agonist	153	ADRB1	Activator
+Epinephrine	agonist	153	ADRB1	Activator
+Labetalol	antagonist	153	ADRB1	Inhibitor
+Dobutamine	agonist	153	ADRB1	Activator
+Levobunolol	antagonist	153	ADRB1	Inhibitor
+Racepinephrine	agonist	153	ADRB1	Activator
+Isoetharine	agonist	153	ADRB1	Activator
+Nebivolol	antagonist	153	ADRB1	Inhibitor
+Atenolol	antagonist	153	ADRB1	Inhibitor
+Levobetaxolol	antagonist	153	ADRB1	Inhibitor
+Sotalol	antagonist	153	ADRB1	Inhibitor
+Isoprenaline	agonist	153	ADRB1	Activator
+Metipranolol	antagonist	153	ADRB1	Inhibitor
+Nadolol	antagonist	153	ADRB1	Inhibitor
+Acebutolol	partial agonist	153	ADRB1	Modulator
+Oxprenolol	antagonist	153	ADRB1	Inhibitor
+Metoprolol	antagonist	153	ADRB1	Inhibitor
+Arbutamine	agonist	153	ADRB1	Activator
+Droxidopa	agonist	153	ADRB1	Activator
+Timolol	antagonist	153	ADRB1	Inhibitor
+Practolol	antagonist	153	ADRB1	Inhibitor
+Penbutolol	antagonist|partial agonist	153	ADRB1	Activator
+Bisoprolol	antagonist	153	ADRB1	Inhibitor
+Betaxolol	antagonist	153	ADRB1	Inhibitor
+Xamoterol	partial agonist	153	ADRB1	Modulator
+Norepinephrine	agonist	153	ADRB1	Activator
+Celiprolol	antagonist	153	ADRB1	Inhibitor
+Ephedrine	agonist	153	ADRB1	Activator
+Carvedilol	antagonist	153	ADRB1	Inhibitor
+Celiprolol	agonist	154	ADRB2	Activator
+Labetalol	antagonist	154	ADRB2	Inhibitor
+Orciprenaline	agonist	154	ADRB2	Activator
+Vilanterol	agonist	154	ADRB2	Activator
+Norepinephrine	agonist	154	ADRB2	Activator
+Epinephrine	agonist	154	ADRB2	Activator
+Terbutaline	agonist	154	ADRB2	Activator
+Arformoterol	agonist	154	ADRB2	Activator
+Pindolol	partial agonist	154	ADRB2	Modulator
+Isoprenaline	agonist|binder	154	ADRB2	Activator
+Penbutolol	antagonist|partial agonist	154	ADRB2	Activator
+Metipranolol	antagonist	154	ADRB2	Inhibitor
+Racepinephrine	agonist	154	ADRB2	Activator
+Timolol	antagonist	154	ADRB2	Inhibitor
+Fenoterol	agonist	154	ADRB2	Activator
+Sotalol	antagonist	154	ADRB2	Inhibitor
+Pirbuterol	agonist	154	ADRB2	Activator
+Ritodrine	agonist	154	ADRB2	Activator
+Levosalbutamol	agonist	154	ADRB2	Activator
+Droxidopa	agonist	154	ADRB2	Activator
+Etafedrine	agonist	154	ADRB2	Activator
+Procaterol	agonist	154	ADRB2	Activator
+Formoterol	agonist	154	ADRB2	Activator
+Indacaterol	agonist	154	ADRB2	Activator
+Salmeterol	agonist	154	ADRB2	Activator
+Olodaterol	agonist	154	ADRB2	Activator
+Levobunolol	antagonist	154	ADRB2	Inhibitor
+Salbutamol	agonist	154	ADRB2	Activator
+Carteolol	antagonist	154	ADRB2	Inhibitor
+Clenbuterol	agonist	154	ADRB2	Activator
+Dipivefrin	agonist	154	ADRB2	Activator
+Ephedrine	agonist	154	ADRB2	Activator
+Protokylol	agonist	154	ADRB2	Activator
+Vibegron	agonist	155	ADRB3	Activator
+Racepinephrine	agonist	155	ADRB3	Activator
+Droxidopa	agonist	155	ADRB3	Activator
+Isoprenaline	agonist	155	ADRB3	Activator
+Mirabegron	agonist	155	ADRB3	Activator
+Norepinephrine	agonist	155	ADRB3	Activator
+Irbesartan	antagonist	185	AGTR1	Inhibitor
+Candesartan cilexetil	antagonist	185	AGTR1	Inhibitor
+Telmisartan	antagonist	185	AGTR1	Inhibitor
+Valsartan	antagonist	185	AGTR1	Inhibitor
+Azilsartan medoxomil	antagonist	185	AGTR1	Inhibitor
+Olmesartan	antagonist	185	AGTR1	Inhibitor
+Losartan	antagonist	185	AGTR1	Inhibitor
+Eprosartan	antagonist	185	AGTR1	Inhibitor
+Tapinarof	agonist	196	AHR	Activator
+Aminolevulinic acid	inducer	210	ALAD	Activator
+Patent Blue	binder	213	ALB	Ligand
+Guanidine	inhibitor	217	ALDH2	Inhibitor
+Disulfiram	inhibitor	217	ALDH2	Inhibitor
+Citric acid	inhibitor	231	AKR1B1	Inhibitor
+Ceritinib	antagonist	238	ALK	Inhibitor
+Brigatinib	inhibitor	238	ALK	Inhibitor
+Lorlatinib	inhibitor	238	ALK	Inhibitor
+Alectinib	inhibitor	238	ALK	Inhibitor
+Crizotinib	inhibitor	238	ALK	Inhibitor
+Gilteritinib	inhibitor	238	ALK	Inhibitor
+Meclofenamic acid	inhibitor	240	ALOX5	Inhibitor
+Balsalazide	inhibitor	240	ALOX5	Inhibitor
+Masoprocol	inhibitor	240	ALOX5	Inhibitor
+Diethylcarbamazine	inhibitor	240	ALOX5	Inhibitor
+Mesalazine	inhibitor	240	ALOX5	Inhibitor
+Sulfasalazine	inhibitor	240	ALOX5	Inhibitor
+Amifostine	inducer	251	ALPG	Activator
+Acarbose	inhibitor	279	AMY2A	Inhibitor
+Faricimab	antagonist	285	ANGPT2	Inhibitor
+Clodronic acid	inhibitor	291	SLC25A4	Inhibitor
+Clodronic acid	inhibitor	292	SLC25A5	Inhibitor
+Clodronic acid	inhibitor	293	SLC25A6	Inhibitor
+Hydrocortisone butyrate	unknown	301	ANXA1	other
+Hydrocortisone probutate	unknown	301	ANXA1	other
+Fluocinolone acetonide	inducer	301	ANXA1	Activator
+Hydrocortisone phosphate	unknown	301	ANXA1	other
+Hydrocortisone acetate	unknown	301	ANXA1	other
+Hydrocortisone cypionate	unknown	301	ANXA1	other
+Hydrocortisone	agonist	301	ANXA1	Activator
+Hydrocortisone valerate	unknown	301	ANXA1	other
+Fluocinolone acetonide	inducer	302	ANXA2	Activator
+Fluocinolone acetonide	inducer	306	ANXA3	Activator
+Fluocinolone acetonide	inducer	307	ANXA4	Activator
+Fluocinolone acetonide	inducer	308	ANXA5	Activator
+Dequalinium	antagonist|inhibitor	331	XIAP	Activator
+Volanesorsen	inhibitor|antisense oligonucleotide	345	APOC3	Activator
+Florbetapir (18F)	binder	351	APP	Ligand
+Flutemetamol (18F)	binder	351	APP	Ligand
+Florbetaben (18F)	binder	351	APP	Ligand
+Lutetium Lu-177 vipivotide tetraxetan	binder	354	KLK3	Ligand
+Gallium Ga-68 gozetotide	binder	354	KLK3	Ligand
+Clascoterone	antagonist	367	AR	Inhibitor
+Testosterone cypionate	agonist	367	AR	Activator
+Methyltestosterone	agonist	367	AR	Activator
+Testosterone undecanoate	agonist	367	AR	Activator
+Testosterone	agonist	367	AR	Activator
+Flutamide	antagonist	367	AR	Inhibitor
+Dienogest	antagonist	367	AR	Inhibitor
+Enzalutamide	inhibitor	367	AR	Inhibitor
+Testosterone enanthate	agonist	367	AR	Activator
+Nilutamide	antagonist	367	AR	Inhibitor
+Darolutamide	antagonist	367	AR	Inhibitor
+Nandrolone decanoate	agonist	367	AR	Activator
+Danazol	agonist	367	AR	Activator
+Drospirenone	antagonist	367	AR	Inhibitor
+Cyproterone acetate	antagonist	367	AR	Inhibitor
+Fluoxymesterone	agonist	367	AR	Activator
+Apalutamide	antagonist	367	AR	Inhibitor
+Oxymetholone	agonist|activator	367	AR	Activator
+Nandrolone phenpropionate	agonist	367	AR	Activator
+Stanozolol	agonist	367	AR	Activator
+Oxandrolone	agonist	367	AR	Activator
+Bicalutamide	antagonist	367	AR	Inhibitor
+Dalteparin	potentiator	462	SERPINC1	Activator
+Nadroparin	potentiator	462	SERPINC1	Activator
+Heparin	potentiator	462	SERPINC1	Activator
+Enoxaparin	potentiator	462	SERPINC1	Activator
+Sulodexide	potentiator	462	SERPINC1	Activator
+Tinzaparin	potentiator	462	SERPINC1	Activator
+Fondaparinux	potentiator	462	SERPINC1	Activator
+Methotrexate	inhibitor	471	ATIC	Inhibitor
+Pemetrexed	inhibitor	471	ATIC	Inhibitor
+Ciclopirox	binder	476	ATP1A1	Ligand
+Acetyldigitoxin	inhibitor	476	ATP1A1	Inhibitor
+Almitrine	binder	476	ATP1A1	Ligand
+Etacrynic acid	inhibitor	476	ATP1A1	Inhibitor
+Ouabain	inhibitor	476	ATP1A1	Inhibitor
+Trichlormethiazide	inhibitor	476	ATP1A1	Inhibitor
+Deslanoside	inhibitor	476	ATP1A1	Inhibitor
+Digoxin	inhibitor	476	ATP1A1	Inhibitor
+Digitoxin	inhibitor	476	ATP1A1	Inhibitor
+Bretylium	inhibitor	476	ATP1A1	Inhibitor
+Cyclothiazide	inhibitor	486	FXYD2	Inhibitor
+Esomeprazole	inhibitor	495	ATP4A	Inhibitor
+Pantoprazole	inhibitor	495	ATP4A	Inhibitor
+Lansoprazole	inhibitor	495	ATP4A	Inhibitor
+Omeprazole	inhibitor	495	ATP4A	Inhibitor
+Rabeprazole	inhibitor	495	ATP4A	Inhibitor
+Dexlansoprazole	inhibitor	495	ATP4A	Inhibitor
+Dexlansoprazole	inhibitor	496	ATP4B	Inhibitor
+Gallium nitrate	inhibitor	526	ATP6V1B2	Inhibitor
+Thonzonium	inhibitor	528	ATP6V1C1	Inhibitor
+Desmopressin	unknown	552	AVPR1A	other
+Conivaptan	antagonist	552	AVPR1A	Inhibitor
+Terlipressin	agonist	552	AVPR1A	Activator
+Lypressin	unknown	552	AVPR1A	other
+Vasopressin	agonist|regulator	552	AVPR1A	Activator
+Lypressin	unknown	553	AVPR1B	other
+Terlipressin	agonist	553	AVPR1B	Activator
+Vasopressin	agonist	553	AVPR1B	Activator
+Desmopressin	unknown	553	AVPR1B	other
+Vasopressin	agonist|regulator	554	AVPR2	Activator
+Terlipressin	agonist	554	AVPR2	Activator
+Desmopressin	agonist	554	AVPR2	Activator
+Lypressin	unknown	554	AVPR2	other
+Tolvaptan	antagonist	554	AVPR2	Inhibitor
+Conivaptan	antagonist	554	AVPR2	Inhibitor
+Gilteritinib	inhibitor	558	AXL	Inhibitor
+Edrophonium	inhibitor	590	BCHE	Inhibitor
+Hexafluronium	inhibitor	590	BCHE	Inhibitor
+Echothiophate	inhibitor	590	BCHE	Inhibitor
+Ketamine	inhibitor	590	BCHE	Inhibitor
+Dipivefrin	substrate	590	BCHE	Activator
+Pyridostigmine	inhibitor	590	BCHE	Inhibitor
+Malathion	inhibitor	590	BCHE	Inhibitor
+Pralidoxime	activator	590	BCHE	Activator
+Rivastigmine	inhibitor	590	BCHE	Inhibitor
+Encorafenib	inhibitor	595	CCND1	Inhibitor
+Arsenic trioxide	antagonist	595	CCND1	Inhibitor
+Paclitaxel	inhibitor	596	BCL2	Inhibitor
+Rasagiline	activator	596	BCL2	Activator
+Venetoclax	antagonist|inhibitor	596	BCL2	Activator
+Idecabtagene vicleucel	binder	608	TNFRSF17	Ligand
+Ciltacabtagene autoleucel	binder	608	TNFRSF17	Ligand
+Teclistamab	antibody	608	TNFRSF17	Modulator
+Imatinib	inhibitor	613	BCR	Inhibitor
+Ponatinib	inhibitor	613	BCR	Inhibitor
+Bosutinib	inhibitor	613	BCR	Inhibitor
+Icatibant	antagonist	624	BDKRB2	Inhibitor
+Gallium nitrate	antagonist	632	BGLAP	Inhibitor
+Betaine	substrate	635	BHMT	Activator
+Riboflavin	product of	645	BLVRB	other
+Dibotermin alfa	ligand	657	BMPR1A	Ligand
+Dibotermin alfa	ligand	659	BMPR2	Ligand
+Regorafenib	inhibitor	673	BRAF	Inhibitor
+Ripretinib	inhibitor	673	BRAF	Inhibitor
+Sorafenib	inhibitor	673	BRAF	Inhibitor
+Dabrafenib	inhibitor	673	BRAF	Inhibitor
+Vemurafenib	inhibitor	673	BRAF	Inhibitor
+Encorafenib	inhibitor	673	BRAF	Inhibitor
+Nicotinamide	product of	683	BST1	other
+Zanubrutinib	inhibitor	695	BTK	Inhibitor
+Ibrutinib	inhibitor	695	BTK	Inhibitor
+Acalabrutinib	inhibitor	695	BTK	Inhibitor
+Human C1-esterase inhibitor	inhibitor	715	C1R	Inhibitor
+Conestat alfa	inhibitor	715	C1R	Inhibitor
+Human C1-esterase inhibitor	inhibitor	716	C1S	Inhibitor
+Sutimlimab	inhibitor|antibody	716	C1S	Inhibitor
+Conestat alfa	inhibitor	716	C1S	Inhibitor
+Pegcetacoplan	binder|regulator	718	C3	Inhibitor
+Human immunoglobulin G	binder	718	C3	Ligand
+Human immunoglobulin G	binder	720	C4A	Ligand
+Human immunoglobulin G	binder	721	C4B	Ligand
+Human immunoglobulin G	binder	721	C4B	Ligand
+Eculizumab	antibody	727	C5	Modulator
+Human immunoglobulin G	binder	727	C5	Ligand
+Ravulizumab	inhibitor	727	C5	Inhibitor
+Avacopan	antagonist	728	C5AR1	Inhibitor
+Diclofenamide	inhibitor	759	CA1	Inhibitor
+Acetazolamide	inhibitor	759	CA1	Inhibitor
+Chlorothiazide	inhibitor	759	CA1	Inhibitor
+Dorzolamide	inhibitor	759	CA1	Inhibitor
+Methazolamide	inhibitor	759	CA1	Inhibitor
+Chlorthalidone	inhibitor	759	CA1	Inhibitor
+Diazoxide	inhibitor	759	CA1	Inhibitor
+Sodium sulfate	inhibitor	759	CA1	Inhibitor
+Quinethazone	inhibitor	759	CA1	Inhibitor
+Methazolamide	inhibitor	760	CA2	Inhibitor
+Sodium sulfate	inhibitor	760	CA2	Inhibitor
+Quinethazone	inhibitor	760	CA2	Inhibitor
+Diazoxide	inhibitor	760	CA2	Inhibitor
+Topiramate	inhibitor	760	CA2	Inhibitor
+Acetazolamide	inhibitor	760	CA2	Inhibitor
+Dorzolamide	inhibitor	760	CA2	Inhibitor
+Chlorothiazide	inhibitor	760	CA2	Inhibitor
+Diclofenamide	inhibitor	760	CA2	Inhibitor
+Brinzolamide	inhibitor	760	CA2	Inhibitor
+Acetazolamide	inhibitor	761	CA3	Inhibitor
+Diclofenamide	inhibitor	762	CA4	Inhibitor
+Methazolamide	inhibitor	762	CA4	Inhibitor
+Dorzolamide	inhibitor	762	CA4	Inhibitor
+Acetazolamide	inhibitor	762	CA4	Inhibitor
+Topiramate	inhibitor	762	CA4	Inhibitor
+Diclofenamide	inhibitor	766	CA7	Inhibitor
+Methazolamide	inhibitor	766	CA7	Inhibitor
+Acetazolamide	inhibitor	766	CA7	Inhibitor
+Acetazolamide	inhibitor	771	CA12	Inhibitor
+Levetiracetam	inhibitor	774	CACNA1B	Inhibitor
+Ziconotide	inhibitor	774	CACNA1B	Inhibitor
+Nicardipine	inhibitor	775	CACNA1C	Inhibitor
+Isradipine	inhibitor	775	CACNA1C	Inhibitor
+Nilvadipine	inhibitor	775	CACNA1C	Inhibitor
+Verapamil	inhibitor	775	CACNA1C	Inhibitor
+Cinnarizine	inhibitor	775	CACNA1C	Inhibitor
+Felodipine	inhibitor	775	CACNA1C	Inhibitor
+Amlodipine	inhibitor	775	CACNA1C	Inhibitor
+Dronedarone	inhibitor	775	CACNA1C	Inhibitor
+Diltiazem	blocker	775	CACNA1C	Inhibitor
+Propiverine	antagonist	775	CACNA1C	Inhibitor
+Nifedipine	inhibitor	775	CACNA1C	Inhibitor
+Nitrendipine	inhibitor	775	CACNA1C	Inhibitor
+Nimodipine	inhibitor	775	CACNA1C	Inhibitor
+Nisoldipine	inhibitor	775	CACNA1C	Inhibitor
+Ibutilide	activator	775	CACNA1C	Activator
+Dronedarone	inhibitor	776	CACNA1D	Inhibitor
+Nifedipine	inhibitor	776	CACNA1D	Inhibitor
+Felodipine	inhibitor	776	CACNA1D	Inhibitor
+Nimodipine	inhibitor	776	CACNA1D	Inhibitor
+Cinnarizine	inhibitor	776	CACNA1D	Inhibitor
+Nitrendipine	inhibitor	776	CACNA1D	Inhibitor
+Isradipine	inhibitor	776	CACNA1D	Inhibitor
+Nisoldipine	inhibitor	776	CACNA1D	Inhibitor
+Nicardipine	inhibitor	776	CACNA1D	Inhibitor
+Lamotrigine	inhibitor	777	CACNA1E	Inhibitor
+Dronedarone	inhibitor	778	CACNA1F	Inhibitor
+Cinnarizine	inhibitor	778	CACNA1F	Inhibitor
+Nimodipine	inhibitor	778	CACNA1F	Inhibitor
+Isradipine	inhibitor	779	CACNA1S	Inhibitor
+Nimodipine	inhibitor	779	CACNA1S	Inhibitor
+Cinnarizine	inhibitor	779	CACNA1S	Inhibitor
+Felodipine	inhibitor	779	CACNA1S	Inhibitor
+Nitrendipine	inhibitor	779	CACNA1S	Inhibitor
+Nisoldipine	inhibitor	779	CACNA1S	Inhibitor
+Pinaverium	antagonist|inhibitor	779	CACNA1S	Activator
+Dronedarone	inhibitor	779	CACNA1S	Inhibitor
+Nicardipine	inhibitor	781	CACNA2D1	Inhibitor
+Pregabalin	unknown	781	CACNA2D1	other
+Isradipine	inhibitor	781	CACNA2D1	Inhibitor
+Nilvadipine	inhibitor	781	CACNA2D1	Inhibitor
+Felodipine	inhibitor	781	CACNA2D1	Inhibitor
+Gabapentin	inhibitor	781	CACNA2D1	Inhibitor
+Nitrendipine	inhibitor	781	CACNA2D1	Inhibitor
+Nisoldipine	inhibitor	781	CACNA2D1	Inhibitor
+Nimodipine	inhibitor	782	CACNB1	Inhibitor
+Dronedarone	inhibitor	782	CACNB1	Inhibitor
+Ibutilide	activator	782	CACNB1	Activator
+Nifedipine	inhibitor	783	CACNB2	Inhibitor
+Nimodipine	inhibitor	783	CACNB2	Inhibitor
+Nilvadipine	inhibitor	783	CACNB2	Inhibitor
+Nisoldipine	inhibitor	783	CACNB2	Inhibitor
+Dronedarone	inhibitor	783	CACNB2	Inhibitor
+Felodipine	inhibitor	783	CACNB2	Inhibitor
+Isradipine	inhibitor	783	CACNB2	Inhibitor
+Nicardipine	inhibitor	783	CACNB2	Inhibitor
+Nitrendipine	inhibitor	783	CACNB2	Inhibitor
+Nimodipine	inhibitor	784	CACNB3	Inhibitor
+Dronedarone	inhibitor	784	CACNB3	Inhibitor
+Dronedarone	inhibitor	785	CACNB4	Inhibitor
+Nimodipine	inhibitor	785	CACNB4	Inhibitor
+Lercanidipine	inhibitor	786	CACNG1	Inhibitor
+Diltiazem	blocker	786	CACNG1	Inhibitor
+Eptinezumab	binder|antibody	796	CALCA	Inhibitor
+Galcanezumab	antibody	796	CALCA	Modulator
+Fremanezumab	binder|antibody	796	CALCA	Inhibitor
+Fremanezumab	binder|antibody	797	CALCB	Inhibitor
+Galcanezumab	antibody	797	CALCB	Modulator
+Eptinezumab	binder|antibody	797	CALCB	Inhibitor
+Pramlintide	agonist	799	CALCR	Activator
+Flunarizine	antagonist	801	CALM1	Inhibitor
+Flunarizine	antagonist	805	CALM2	Inhibitor
+Voclosporin	binder	819	CAMLG	Ligand
+Cyclosporine	binder	819	CAMLG	Ligand
+Glycyrrhizic acid	antagonist	836	CASP3	Inhibitor
+Calcium Phosphate	agonist	846	CASR	Activator
+Calcium citrate	agonist	846	CASR	Activator
+Cinacalcet	agonist	846	CASR	Activator
+Calcium phosphate dihydrate	unknown	846	CASR	other
+Etelcalcetide	agonist	846	CASR	Activator
+Fomepizole	inhibitor	847	CAT	Inhibitor
+Ceruletide	inducer	886	CCKAR	Activator
+Pentagastrin	agonist	887	CCKBR	Activator
+Muromonab	unknown	915	CD3D	other
+Blinatumomab	antibody|activator	915	CD3D	Activator
+Mosunetuzumab	binder	916	CD3E	Ligand
+Muromonab	binder	916	CD3E	Ligand
+Teplizumab	antibody	916	CD3E	Modulator
+Muromonab	unknown	917	CD3G	other
+Muromonab	unknown	919	CD247	other
+Antithymocyte immunoglobulin (rabbit)	unknown	920	CD4	other
+Tisagenlecleucel	antibody	930	CD19	Modulator
+Axicabtagene ciloleucel	antibody	930	CD19	Modulator
+Inebilizumab	binder	930	CD19	Ligand
+Brexucabtagene autoleucel	binder	930	CD19	Ligand
+Blinatumomab	antibody|activator|regulator	930	CD19	Activator
+Tositumomab	antibody	931	MS4A1	Modulator
+Ibritumomab tiuxetan	antibody|regulator	931	MS4A1	Inhibitor
+Ublituximab	binder|antibody	931	MS4A1	Inhibitor
+Ofatumumab	antibody|regulator	931	MS4A1	Inhibitor
+Obinutuzumab	antibody|regulator	931	MS4A1	Inhibitor
+Ocrelizumab	antagonist|antibody	931	MS4A1	Activator
+Mosunetuzumab	binder	931	MS4A1	Ligand
+Rituximab	antibody|regulator	931	MS4A1	Inhibitor
+Inotuzumab ozogamicin	antibody|regulator	933	CD22	Inhibitor
+Moxetumomab pasudotox	binder|antibody|regulator	933	CD22	Inhibitor
+Belatacept	antagonist	941	CD80	Inhibitor
+Abatacept	antagonist	941	CD80	Inhibitor
+Antithymocyte immunoglobulin (rabbit)	binder	942	CD86	Ligand
+Belatacept	antagonist	942	CD86	Inhibitor
+Abatacept	antagonist	942	CD86	Inhibitor
+Brentuximab vedotin	binder|antibody|regulator	943	TNFRSF8	Inhibitor
+Gemtuzumab ozogamicin	antibody|regulator	945	CD33	Inhibitor
+Hyaluronic acid	binder	960	CD44	Ligand
+Polatuzumab vedotin	antibody	974	CD79B	Modulator
+Cedazuridine	inhibitor	978	CDA	Inhibitor
+Ribociclib	antagonist|inhibitor	1019	CDK4	Activator
+Palbociclib	inhibitor	1019	CDK4	Inhibitor
+Abemaciclib	inhibitor	1019	CDK4	Inhibitor
+Trilaciclib	inhibitor	1019	CDK4	Inhibitor
+Trilaciclib	inhibitor	1021	CDK6	Inhibitor
+Palbociclib	inhibitor	1021	CDK6	Inhibitor
+Ribociclib	antagonist|inhibitor	1021	CDK6	Activator
+Abemaciclib	inhibitor	1021	CDK6	Inhibitor
+Alemtuzumab	antibody	1043	CD52	Modulator
+Lumacaftor	modulator	1080	CFTR	Inhibitor
+Tezacaftor	activator	1080	CFTR	Activator
+Ivacaftor	potentiator	1080	CFTR	Activator
+Elexacaftor	modulator	1080	CFTR	Inhibitor
+Crofelemer	antagonist	1080	CFTR	Inhibitor
+Biperiden	antagonist	1128	CHRM1	Inhibitor
+Methscopolamine bromide	antagonist	1128	CHRM1	Inhibitor
+Flavoxate	antagonist	1128	CHRM1	Inhibitor
+Pirenzepine	antagonist	1128	CHRM1	Inhibitor
+Dicyclomine	antagonist	1128	CHRM1	Inhibitor
+Homatropine	antagonist	1128	CHRM1	Inhibitor
+Benzatropine	antagonist	1128	CHRM1	Inhibitor
+Anisotropine methylbromide	antagonist	1128	CHRM1	Inhibitor
+Glycopyrronium	antagonist	1128	CHRM1	Inhibitor
+Buclizine	antagonist	1128	CHRM1	Inhibitor
+Scopolamine	antagonist	1128	CHRM1	Inhibitor
+Procyclidine	antagonist	1128	CHRM1	Inhibitor
+Tropicamide	antagonist	1128	CHRM1	Inhibitor
+Clidinium	antagonist	1128	CHRM1	Inhibitor
+Triflupromazine	antagonist	1128	CHRM1	Inhibitor
+Homatropine methylbromide	antagonist	1128	CHRM1	Inhibitor
+Hyoscyamine	antagonist	1128	CHRM1	Inhibitor
+Aclidinium	antagonist	1128	CHRM1	Inhibitor
+Mepenzolate	antagonist	1128	CHRM1	Inhibitor
+Tolterodine	antagonist	1128	CHRM1	Inhibitor
+Oxyphencyclimine	antagonist	1128	CHRM1	Inhibitor
+Cyclopentolate	antagonist	1128	CHRM1	Inhibitor
+Propantheline	antagonist	1128	CHRM1	Inhibitor
+Metoclopramide	agonist	1128	CHRM1	Activator
+Profenamine	antagonist	1128	CHRM1	Inhibitor
+Trihexyphenidyl	antagonist	1128	CHRM1	Inhibitor
+Propiverine	antagonist	1128	CHRM1	Inhibitor
+Oxybutynin	antagonist	1128	CHRM1	Inhibitor
+Atropine	antagonist	1128	CHRM1	Inhibitor
+Carbamoylcholine	agonist	1128	CHRM1	Activator
+Cycrimine	antagonist	1128	CHRM1	Inhibitor
+Cevimeline	agonist	1128	CHRM1	Activator
+Trimebutine	antagonist	1128	CHRM1	Inhibitor
+Dosulepin	antagonist	1128	CHRM1	Inhibitor
+Ipratropium	antagonist	1128	CHRM1	Inhibitor
+Pilocarpine	agonist	1128	CHRM1	Activator
+Oxyphenonium	antagonist	1128	CHRM1	Inhibitor
+Dimetindene	antagonist	1129	CHRM2	Inhibitor
+Homatropine methylbromide	antagonist	1129	CHRM2	Inhibitor
+Hyoscyamine	antagonist	1129	CHRM2	Inhibitor
+Oxybutynin	antagonist	1129	CHRM2	Inhibitor
+Procyclidine	antagonist	1129	CHRM2	Inhibitor
+Homatropine	antagonist	1129	CHRM2	Inhibitor
+Dosulepin	antagonist	1129	CHRM2	Inhibitor
+Glycopyrronium	antagonist	1129	CHRM2	Inhibitor
+Atropine	antagonist	1129	CHRM2	Inhibitor
+Triflupromazine	antagonist	1129	CHRM2	Inhibitor
+Flavoxate	antagonist	1129	CHRM2	Inhibitor
+Oxyphencyclimine	antagonist	1129	CHRM2	Inhibitor
+Tropicamide	antagonist	1129	CHRM2	Inhibitor
+Scopolamine	antagonist	1129	CHRM2	Inhibitor
+Propiverine	antagonist	1129	CHRM2	Inhibitor
+Pilocarpine	agonist	1129	CHRM2	Activator
+Carbamoylcholine	agonist	1129	CHRM2	Activator
+Aclidinium	antagonist	1129	CHRM2	Inhibitor
+Methscopolamine bromide	antagonist	1129	CHRM2	Inhibitor
+Cryptenamine	antagonist	1129	CHRM2	Inhibitor
+Rocuronium	antagonist	1129	CHRM2	Inhibitor
+Trimebutine	antagonist	1129	CHRM2	Inhibitor
+Umeclidinium	antagonist	1129	CHRM2	Inhibitor
+Gallamine triethiodide	antagonist	1129	CHRM2	Inhibitor
+Ipratropium	antagonist	1129	CHRM2	Inhibitor
+Tolterodine	antagonist	1129	CHRM2	Inhibitor
+Anisotropine methylbromide	antagonist	1129	CHRM2	Inhibitor
+Methacholine	agonist	1129	CHRM2	Activator
+Tiotropium	antagonist	1131	CHRM3	Inhibitor
+Bethanechol	agonist	1131	CHRM3	Activator
+Methacholine	agonist	1131	CHRM3	Activator
+Fesoterodine	antagonist	1131	CHRM3	Inhibitor
+Isopropamide	antagonist	1131	CHRM3	Inhibitor
+Aclidinium	antagonist	1131	CHRM3	Inhibitor
+Trimebutine	antagonist	1131	CHRM3	Inhibitor
+Hexocyclium	antagonist	1131	CHRM3	Inhibitor
+Oxybutynin	antagonist	1131	CHRM3	Inhibitor
+Tolterodine	antagonist	1131	CHRM3	Inhibitor
+Procyclidine	antagonist	1131	CHRM3	Inhibitor
+Darifenacin	antagonist	1131	CHRM3	Inhibitor
+Homatropine methylbromide	antagonist	1131	CHRM3	Inhibitor
+Solifenacin	antagonist	1131	CHRM3	Inhibitor
+Dosulepin	antagonist	1131	CHRM3	Inhibitor
+Ipratropium	antagonist	1131	CHRM3	Inhibitor
+Umeclidinium	antagonist	1131	CHRM3	Inhibitor
+Butylscopolamine	antagonist	1131	CHRM3	Inhibitor
+Atropine	antagonist	1131	CHRM3	Inhibitor
+Anisotropine methylbromide	antagonist	1131	CHRM3	Inhibitor
+Homatropine	antagonist	1131	CHRM3	Inhibitor
+Mepenzolate	antagonist	1131	CHRM3	Inhibitor
+Scopolamine	antagonist	1131	CHRM3	Inhibitor
+Glycopyrronium	antagonist	1131	CHRM3	Inhibitor
+Cevimeline	agonist	1131	CHRM3	Activator
+Propiverine	antagonist	1131	CHRM3	Inhibitor
+Oxyphencyclimine	antagonist	1131	CHRM3	Inhibitor
+Pilocarpine	agonist	1131	CHRM3	Activator
+Homatropine methylbromide	antagonist	1132	CHRM4	Inhibitor
+Scopolamine	antagonist	1132	CHRM4	Inhibitor
+Homatropine	antagonist	1132	CHRM4	Inhibitor
+Tropicamide	antagonist	1132	CHRM4	Inhibitor
+Atropine	antagonist	1132	CHRM4	Inhibitor
+Dosulepin	antagonist	1132	CHRM4	Inhibitor
+Glycopyrronium	antagonist	1132	CHRM4	Inhibitor
+Aclidinium	antagonist	1132	CHRM4	Inhibitor
+Trimebutine	antagonist	1132	CHRM4	Inhibitor
+Procyclidine	antagonist	1132	CHRM4	Inhibitor
+Tolterodine	antagonist	1132	CHRM4	Inhibitor
+Aclidinium	antagonist	1133	CHRM5	Inhibitor
+Dosulepin	antagonist	1133	CHRM5	Inhibitor
+Scopolamine	antagonist	1133	CHRM5	Inhibitor
+Atropine	antagonist	1133	CHRM5	Inhibitor
+Homatropine methylbromide	antagonist	1133	CHRM5	Inhibitor
+Tolterodine	antagonist	1133	CHRM5	Inhibitor
+Homatropine	antagonist	1133	CHRM5	Inhibitor
+Propiverine	unknown	1133	CHRM5	other
+Pipecuronium	antagonist	1135	CHRNA2	Inhibitor
+Metocurine	antagonist	1135	CHRNA2	Inhibitor
+Tubocurarine	antagonist	1135	CHRNA2	Inhibitor
+Rocuronium	antagonist	1135	CHRNA2	Inhibitor
+Carbamoylcholine	agonist	1135	CHRNA2	Activator
+Pancuronium	antagonist	1135	CHRNA2	Inhibitor
+Atracurium besylate	antagonist	1135	CHRNA2	Inhibitor
+Doxacurium	antagonist	1135	CHRNA2	Inhibitor
+Vecuronium	antagonist	1135	CHRNA2	Inhibitor
+Decamethonium	partial agonist	1135	CHRNA2	Modulator
+Mivacurium	antagonist	1135	CHRNA2	Inhibitor
+Mecamylamine	antagonist	1135	CHRNA2	Inhibitor
+Pentolinium	antagonist	1136	CHRNA3	Inhibitor
+Varenicline	partial agonist	1137	CHRNA4	Modulator
+Nicotine	agonist	1137	CHRNA4	Activator
+Methadone	agonist	1139	CHRNA7	Activator
+Galantamine	allosteric modulator	1139	CHRNA7	Modulator
+Nicotine	agonist	1139	CHRNA7	Activator
+Nicotine	agonist	1141	CHRNB2	Activator
+Pentolinium	antagonist	1143	CHRNB4	Inhibitor
+Creatine	ligand	1152	CKB	Ligand
+Creatine	ligand	1158	CKM	Ligand
+Creatine	ligand	1159	CKMT1B	Ligand
+Creatine	ligand	1160	CKMT2	Ligand
+Lubiprostone	inducer	1181	CLCN2	Activator
+Mogamulizumab	antagonist	1233	CCR4	Inhibitor
+Maraviroc	antagonist|inhibitor	1234	CCR5	Activator
+Sulfinpyrazone	inhibitor	1244	ABCC2	Inhibitor
+Cannabidiol	antagonist|modulator	1268	CNR1	Activator
+Dronabinol	agonist	1268	CNR1	Activator
+Rimonabant	antagonist	1268	CNR1	Inhibitor
+Nabilone	partial agonist	1268	CNR1	Modulator
+Dronabinol	agonist	1269	CNR2	Activator
+Nabilone	partial agonist	1269	CNR2	Modulator
+Vonicog alfa	binder	1277	COL1A1	Ligand
+Von Willebrand factor human	binder	1277	COL1A1	Ligand
+Collagenase clostridium histolyticum	binder	1277	COL1A1	Ligand
+Collagenase clostridium histolyticum	binder	1280	COL2A1	Ligand
+Collagenase clostridium histolyticum	binder	1281	COL3A1	Ligand
+Opicapone	inhibitor	1312	COMT	Inhibitor
+Entacapone	inhibitor	1312	COMT	Inhibitor
+Prothrombin	agonist	1361	CPB2	Activator
+Carglumic acid	allosteric modulator	1373	CPS1	Modulator
+Perhexiline	inhibitor	1374	CPT1A	Inhibitor
+Perhexiline	inhibitor	1376	CPT2	Inhibitor
+Pexidartinib	inhibitor	1436	CSF1R	Inhibitor
+Sunitinib	inhibitor	1436	CSF1R	Inhibitor
+Sargramostim	agonist	1438	CSF2RA	Activator
+Foreskin keratinocyte (neonatal)	agonist	1438	CSF2RA	Activator
+Sargramostim	agonist	1439	CSF2RB	Activator
+Lenograstim	agonist	1441	CSF3R	Activator
+Eflapegrastim	ligand	1441	CSF3R	Ligand
+Filgrastim	stimulator	1441	CSF3R	Activator
+Pegfilgrastim	agonist	1441	CSF3R	Activator
+Lipegfilgrastim	agonist	1441	CSF3R	Activator
+Ipilimumab	inhibitor|antibody	1493	CTLA4	Inhibitor
+Tremelimumab	inhibitor|antibody	1493	CTLA4	Inhibitor
+Cobicistat	substrate|inhibitor	1576	CYP3A4	Activator
+Aminoglutethimide	inhibitor	1583	CYP11A1	Inhibitor
+Levoketoconazole	inhibitor	1583	CYP11A1	Inhibitor
+Osilodrostat	inhibitor	1584	CYP11B1	Inhibitor
+Levoketoconazole	inhibitor	1584	CYP11B1	Inhibitor
+Mitotane	inducer	1584	CYP11B1	Activator
+Metyrapone	inhibitor	1584	CYP11B1	Inhibitor
+Levoketoconazole	inhibitor	1585	CYP11B2	Inhibitor
+Levoketoconazole	inhibitor	1586	CYP17A1	Inhibitor
+Abiraterone	inhibitor	1586	CYP17A1	Inhibitor
+Ketoconazole	inhibitor	1586	CYP17A1	Inhibitor
+Exemestane	inhibitor	1588	CYP19A1	Inhibitor
+Aminoglutethimide	inhibitor	1588	CYP19A1	Inhibitor
+Testolactone	inhibitor	1588	CYP19A1	Inhibitor
+Letrozole	antagonist	1588	CYP19A1	Inhibitor
+Anastrozole	inhibitor	1588	CYP19A1	Inhibitor
+Itraconazole	inhibitor	1595	CYP51A1	Inhibitor
+Dopamine	ligand	1621	DBH	Ligand
+Disulfiram	inhibitor	1621	DBH	Inhibitor
+Fludarabine	agonist	1633	DCK	Activator
+Perindopril	inhibitor	1636	ACE	Inhibitor
+Enalaprilat	inhibitor	1636	ACE	Inhibitor
+Lisinopril	inhibitor	1636	ACE	Inhibitor
+Quinapril	inhibitor	1636	ACE	Inhibitor
+Zofenopril	inhibitor	1636	ACE	Inhibitor
+Benazepril	inhibitor	1636	ACE	Inhibitor
+Fosinopril	inhibitor	1636	ACE	Inhibitor
+Spirapril	inhibitor	1636	ACE	Inhibitor
+Rescinnamine	inhibitor	1636	ACE	Inhibitor
+Moexipril	inhibitor	1636	ACE	Inhibitor
+Trandolapril	inhibitor	1636	ACE	Inhibitor
+Ramipril	inhibitor	1636	ACE	Inhibitor
+Cilazapril	inhibitor	1636	ACE	Inhibitor
+Enalapril	inhibitor	1636	ACE	Inhibitor
+Captopril	inhibitor	1636	ACE	Inhibitor
+Carbidopa	inhibitor	1644	DDC	Inhibitor
+Methyldopa	inhibitor	1644	DDC	Inhibitor
+Benserazide	inhibitor	1644	DDC	Inhibitor
+Ursodeoxycholic acid	inducer	1646	AKR1C2	Activator
+Pralatrexate	substrate|inhibitor	1719	DHFR	Activator
+Proguanil	inhibitor	1719	DHFR	Inhibitor
+Pemetrexed	inhibitor	1719	DHFR	Inhibitor
+Pyrimethamine	inhibitor	1719	DHFR	Inhibitor
+Trimetrexate	inhibitor	1719	DHFR	Inhibitor
+Methotrexate	inhibitor	1719	DHFR	Inhibitor
+Leflunomide	inhibitor	1723	DHODH	Inhibitor
+Golodirsen	inducer	1756	DMD	Activator
+Azacitidine	inhibitor	1786	DNMT1	Inhibitor
+Decitabine	inhibitor	1786	DNMT1	Inhibitor
+Cilastatin	inhibitor	1800	DPEP1	Inhibitor
+Sitagliptin	inhibitor	1803	DPP4	Inhibitor
+Saxagliptin	inhibitor	1803	DPP4	Inhibitor
+Alogliptin	inhibitor	1803	DPP4	Inhibitor
+Linagliptin	inhibitor	1803	DPP4	Inhibitor
+Vildagliptin	inhibitor	1803	DPP4	Inhibitor
+Tegafur-uracil	antagonist	1806	DPYD	Inhibitor
+Fenoldopam	agonist	1812	DRD1	Activator
+Perphenazine	antagonist	1812	DRD1	Inhibitor
+Pipotiazine	antagonist	1812	DRD1	Inhibitor
+Chlorpromazine	antagonist	1812	DRD1	Inhibitor
+Levodopa	agonist	1812	DRD1	Activator
+Dopamine	agonist	1812	DRD1	Activator
+Methylergometrine	antagonist	1812	DRD1	Inhibitor
+Fluphenazine	antagonist	1812	DRD1	Inhibitor
+Thiothixene	antagonist	1812	DRD1	Inhibitor
+Triflupromazine	antagonist	1812	DRD1	Inhibitor
+Zuclopenthixol	antagonist	1812	DRD1	Inhibitor
+Periciazine	antagonist	1812	DRD1	Inhibitor
+Lisuride	agonist	1813	DRD2	Activator
+Aripiprazole	antagonist|partial agonist	1813	DRD2	Activator
+Trifluoperazine	antagonist	1813	DRD2	Inhibitor
+Quetiapine	antagonist	1813	DRD2	Inhibitor
+Pipotiazine	antagonist	1813	DRD2	Inhibitor
+Amantadine	agonist	1813	DRD2	Activator
+Iloperidone	antagonist	1813	DRD2	Inhibitor
+Ropinirole	agonist	1813	DRD2	Activator
+Buspirone	antagonist	1813	DRD2	Inhibitor
+Risperidone	antagonist	1813	DRD2	Inhibitor
+Asenapine	antagonist	1813	DRD2	Inhibitor
+Methotrimeprazine	antagonist	1813	DRD2	Inhibitor
+Fluspirilene	antagonist	1813	DRD2	Inhibitor
+Metoclopramide	antagonist	1813	DRD2	Inhibitor
+Levodopa	agonist	1813	DRD2	Activator
+Cabergoline	agonist	1813	DRD2	Activator
+Mesoridazine	antagonist	1813	DRD2	Inhibitor
+Promazine	antagonist	1813	DRD2	Inhibitor
+Dopamine	agonist	1813	DRD2	Activator
+Brexpiprazole	agonist|partial agonist	1813	DRD2	Activator
+Pramipexole	agonist	1813	DRD2	Activator
+Pimozide	antagonist	1813	DRD2	Inhibitor
+Dihydro-alpha-ergocryptine	agonist	1813	DRD2	Activator
+Sulpiride	antagonist	1813	DRD2	Inhibitor
+Apomorphine	agonist	1813	DRD2	Activator
+Lumateperone	partial agonist	1813	DRD2	Modulator
+Fluphenazine	antagonist	1813	DRD2	Inhibitor
+Aripiprazole lauroxil	partial agonist	1813	DRD2	Modulator
+Rotigotine	agonist	1813	DRD2	Activator
+Loxapine	antagonist	1813	DRD2	Inhibitor
+Zuclopenthixol	antagonist	1813	DRD2	Inhibitor
+Prochlorperazine	antagonist	1813	DRD2	Inhibitor
+Triflupromazine	antagonist	1813	DRD2	Inhibitor
+Haloperidol	antagonist	1813	DRD2	Inhibitor
+Clozapine	antagonist	1813	DRD2	Inhibitor
+Lurasidone	antagonist	1813	DRD2	Inhibitor
+Domperidone	antagonist	1813	DRD2	Inhibitor
+Amisulpride	antagonist	1813	DRD2	Inhibitor
+Paliperidone	antagonist	1813	DRD2	Inhibitor
+Acetophenazine	antagonist	1813	DRD2	Inhibitor
+Ziprasidone	antagonist	1813	DRD2	Inhibitor
+Molindone	antagonist	1813	DRD2	Inhibitor
+Thiothixene	antagonist	1813	DRD2	Inhibitor
+Perphenazine	antagonist	1813	DRD2	Inhibitor
+Droperidol	antagonist	1813	DRD2	Inhibitor
+Chlorpromazine	antagonist	1813	DRD2	Inhibitor
+Olanzapine	antagonist	1813	DRD2	Inhibitor
+Levodopa	agonist	1814	DRD3	Activator
+Pimozide	antagonist	1814	DRD3	Inhibitor
+Dopamine	agonist	1814	DRD3	Activator
+Apomorphine	agonist	1814	DRD3	Activator
+Amisulpride	antagonist	1814	DRD3	Inhibitor
+Ropinirole	agonist	1814	DRD3	Activator
+Lisuride	agonist	1814	DRD3	Activator
+Rotigotine	agonist	1814	DRD3	Activator
+Domperidone	antagonist	1814	DRD3	Inhibitor
+Pramipexole	agonist	1814	DRD3	Activator
+Paliperidone	antagonist	1814	DRD3	Inhibitor
+Dihydro-alpha-ergocryptine	partial agonist	1814	DRD3	Modulator
+Pramipexole	agonist	1815	DRD4	Activator
+Paliperidone	antagonist	1815	DRD4	Inhibitor
+Levodopa	agonist	1815	DRD4	Activator
+Lisuride	agonist	1815	DRD4	Activator
+Dopamine	agonist	1815	DRD4	Activator
+Apomorphine	agonist	1815	DRD4	Activator
+Rotigotine	agonist	1815	DRD4	Activator
+Flibanserin	antagonist|agonist	1815	DRD4	Modulator
+Zuclopenthixol	antagonist	1816	DRD5	Inhibitor
+Fenoldopam	agonist	1816	DRD5	Activator
+Levodopa	agonist	1816	DRD5	Activator
+Dopamine	agonist	1816	DRD5	Activator
+Tipiracil	inhibitor	1890	TYMP	Inhibitor
+Omega-3-carboxylic acids	potentiator	1892	ECHS1	Activator
+Ponesimod	agonist|modulator|regulator	1901	S1PR1	Activator
+Ozanimod	agonist	1901	S1PR1	Activator
+Fingolimod	modulator	1901	S1PR1	Inhibitor
+Fingolimod	modulator	1903	S1PR3	Inhibitor
+Bosentan	antagonist	1909	EDNRA	Inhibitor
+Macitentan	antagonist	1909	EDNRA	Inhibitor
+Ambrisentan	antagonist	1909	EDNRA	Inhibitor
+Bosentan	antagonist	1910	EDNRB	Inhibitor
+Moxetumomab pasudotox	inactivator	1938	EEF2	Activator
+Sucralfate	inducer	1950	EGF	Activator
+Cetuximab	binder	1956	EGFR	Ligand
+Brigatinib	inhibitor	1956	EGFR	Inhibitor
+Neratinib	inhibitor	1956	EGFR	Inhibitor
+Afatinib	inhibitor	1956	EGFR	Inhibitor
+Mobocertinib	inhibitor	1956	EGFR	Inhibitor
+Osimertinib	inhibitor|regulator	1956	EGFR	Inhibitor
+Gefitinib	antagonist	1956	EGFR	Inhibitor
+Erlotinib	antagonist	1956	EGFR	Inhibitor
+Foreskin keratinocyte (neonatal)	agonist	1956	EGFR	Activator
+Necitumumab	antagonist	1956	EGFR	Inhibitor
+Panitumumab	suppressor	1956	EGFR	Inhibitor
+Amivantamab	antibody|downregulator	1956	EGFR	Inhibitor
+Lapatinib	antagonist	1956	EGFR	Inhibitor
+Dacomitinib	inhibitor	1956	EGFR	Inhibitor
+Regorafenib	inhibitor	1969	EPHA2	Inhibitor
+Dasatinib	antagonist	1969	EPHA2	Inhibitor
+Alpha-1-proteinase inhibitor	inhibitor	1991	ELANE	Inhibitor
+Belzutifan	inhibitor	2034	EPAS1	Inhibitor
+Darbepoetin alfa	agonist	2057	EPOR	Activator
+Methoxy polyethylene glycol-epoetin beta	stimulator	2057	EPOR	Activator
+Peginesatide	stimulator	2057	EPOR	Activator
+Erythropoietin	agonist	2057	EPOR	Activator
+Pertuzumab	binder|antibody	2064	ERBB2	Inhibitor
+Margetuximab	antagonist	2064	ERBB2	Inhibitor
+Lapatinib	antagonist	2064	ERBB2	Inhibitor
+Trastuzumab	binder|antibody	2064	ERBB2	Inhibitor
+Trastuzumab emtansine	antibody	2064	ERBB2	Modulator
+Afatinib	inhibitor	2064	ERBB2	Inhibitor
+Tucatinib	inhibitor	2064	ERBB2	Inhibitor
+Afatinib	inhibitor	2066	ERBB4	Inhibitor
+Estradiol benzoate	agonist	2099	ESR1	Activator
+Raloxifene	agonist	2099	ESR1	Activator
+"Synthetic Conjugated Estrogens, A"	ligand	2099	ESR1	Ligand
+Desogestrel	agonist	2099	ESR1	Activator
+Fluoxymesterone	antagonist	2099	ESR1	Inhibitor
+Toremifene	modulator	2099	ESR1	Inhibitor
+Clomifene	antagonist|agonist	2099	ESR1	Modulator
+Conjugated estrogens	agonist	2099	ESR1	Activator
+Estradiol dienanthate	agonist	2099	ESR1	Activator
+Ethynodiol diacetate	agonist	2099	ESR1	Activator
+Quinestrol	agonist|modulator	2099	ESR1	Activator
+Bazedoxifene	antagonist|agonist	2099	ESR1	Modulator
+Fulvestrant	antagonist	2099	ESR1	Inhibitor
+Estradiol valerate	agonist	2099	ESR1	Activator
+Estradiol acetate	agonist	2099	ESR1	Activator
+Medroxyprogesterone acetate	agonist	2099	ESR1	Activator
+Estrone	agonist	2099	ESR1	Activator
+Lasofoxifene	antagonist|agonist|negative modulator	2099	ESR1	Activator
+Norgestimate	agonist	2099	ESR1	Activator
+Levonorgestrel	unknown	2099	ESR1	other
+Estrone sulfate	agonist	2099	ESR1	Activator
+Ospemifene	antagonist|agonist	2099	ESR1	Modulator
+"Synthetic Conjugated Estrogens, B"	ligand	2099	ESR1	Ligand
+Estramustine	agonist	2099	ESR1	Activator
+Danazol	agonist	2099	ESR1	Activator
+Dienestrol	agonist	2099	ESR1	Activator
+Tibolone	antagonist|agonist	2099	ESR1	Modulator
+Tamoxifen	antagonist|agonist	2099	ESR1	Modulator
+Ethinylestradiol	agonist	2099	ESR1	Activator
+Estradiol cypionate	agonist	2099	ESR1	Activator
+Estradiol	agonist	2099	ESR1	Activator
+Estriol	agonist	2099	ESR1	Activator
+Mestranol	agonist	2099	ESR1	Activator
+Polyestradiol phosphate	agonist	2099	ESR1	Activator
+Lasofoxifene	agonist	2100	ESR2	Activator
+Estradiol benzoate	agonist	2100	ESR2	Activator
+Estradiol	agonist	2100	ESR2	Activator
+Estradiol dienanthate	agonist	2100	ESR2	Activator
+Estradiol valerate	agonist	2100	ESR2	Activator
+Estramustine	unknown	2100	ESR2	other
+Conjugated estrogens	agonist	2100	ESR2	Activator
+Estradiol cypionate	agonist	2100	ESR2	Activator
+Estradiol acetate	agonist	2100	ESR2	Activator
+Raloxifene	agonist	2100	ESR2	Activator
+Tamoxifen	antagonist|agonist	2100	ESR2	Modulator
+Metformin	inhibitor	2110	ETFDH	Inhibitor
+Tazemetostat	inhibitor	2146	EZH2	Inhibitor
+Anti-inhibitor coagulant complex	agonist	2147	F2	Activator
+Dabigatran etexilate	inhibitor	2147	F2	Inhibitor
+Turoctocog alfa	binder	2147	F2	Ligand
+Argatroban	inhibitor	2147	F2	Inhibitor
+Conestat alfa	inhibitor	2147	F2	Inhibitor
+Turoctocog alfa pegol	binder	2147	F2	Ligand
+Thrombomodulin Alfa	inhibitor	2147	F2	Inhibitor
+Antithrombin Alfa	inhibitor	2147	F2	Inhibitor
+Bivalirudin	inhibitor	2147	F2	Inhibitor
+Vorapaxar	antagonist	2149	F2R	Inhibitor
+Tisotumab vedotin	antibody	2152	F3	Modulator
+Coagulation factor VII human	activator	2152	F3	Activator
+Protein C	inactivator	2153	F5	Activator
+Protein S human	antagonist	2153	F5	Inhibitor
+Thrombin alfa	activator	2153	F5	Activator
+Anti-inhibitor coagulant complex	agonist	2153	F5	Activator
+Human thrombin	activator	2153	F5	Activator
+Thrombin	activator	2153	F5	Activator
+Coagulation Factor IX Human	ligand	2155	F7	Ligand
+Anti-inhibitor coagulant complex	agonist	2155	F7	Activator
+Coagulation Factor IX (Recombinant)	ligand	2155	F7	Ligand
+Nonacog beta pegol	cofactor	2155	F7	other
+Nonacog beta pegol	cofactor	2157	F8	other
+Human thrombin	activator	2157	F8	Activator
+Protein C	inactivator	2157	F8	Activator
+Damoctocog alfa pegol	binder	2157	F8	Ligand
+Thrombin alfa	activator	2157	F8	Activator
+Vonicog alfa	stabilization	2157	F8	other
+Coagulation Factor IX (Recombinant)	cofactor	2157	F8	other
+Anti-inhibitor coagulant complex	agonist	2157	F8	Activator
+Von Willebrand factor human	carrier|stabilization	2157	F8	other
+Coagulation Factor IX Human	cofactor	2157	F8	other
+Thrombin	activator	2157	F8	Activator
+Emicizumab	cofactor	2158	F9	other
+Turoctocog alfa	activator	2158	F9	Activator
+Antihemophilic factor human	activator	2158	F9	Activator
+Coagulation factor VII human	activator	2158	F9	Activator
+Turoctocog alfa pegol	activator	2158	F9	Activator
+Lonoctocog alfa	cofactor	2158	F9	other
+"Antihemophilic factor, human recombinant"	cofactor	2158	F9	other
+Moroctocog alfa	cofactor	2158	F9	other
+Emicizumab	activator	2159	F10	Activator
+Nonacog beta pegol	agonist	2159	F10	Activator
+Edoxaban	inhibitor	2159	F10	Inhibitor
+Bemiparin	antagonist	2159	F10	Inhibitor
+Coagulation factor VII human	activator	2159	F10	Activator
+Turoctocog alfa pegol	activator	2159	F10	Activator
+Coagulation Factor IX (Recombinant)	activator	2159	F10	Activator
+Moroctocog alfa	activator	2159	F10	Activator
+Lonoctocog alfa	activator	2159	F10	Activator
+Antihemophilic factor human	activator	2159	F10	Activator
+Rivaroxaban	antagonist	2159	F10	Inhibitor
+Anti-inhibitor coagulant complex	agonist	2159	F10	Activator
+Antithrombin Alfa	inhibitor	2159	F10	Inhibitor
+Coagulation Factor IX Human	activator	2159	F10	Activator
+Heparin	inhibitor	2159	F10	Inhibitor
+Enoxaparin	inhibitor	2159	F10	Inhibitor
+Apixaban	inhibitor	2159	F10	Inhibitor
+"Antihemophilic factor, human recombinant"	activator	2159	F10	Activator
+Fondaparinux	inhibitor	2159	F10	Inhibitor
+Albutrepenonacog alfa	activator	2159	F10	Activator
+Turoctocog alfa	activator	2159	F10	Activator
+Betrixaban	inhibitor	2159	F10	Inhibitor
+Protein S human	antagonist	2159	F10	Inhibitor
+Human C1-esterase inhibitor	inhibitor	2160	F11	Inhibitor
+Human thrombin	activator	2160	F11	Activator
+Coagulation Factor IX Human	ligand	2160	F11	Ligand
+Thrombin	activator	2160	F11	Activator
+Conestat alfa	inhibitor	2160	F11	Inhibitor
+Coagulation Factor IX (Recombinant)	ligand	2160	F11	Ligand
+Human C1-esterase inhibitor	inhibitor	2161	F12	Inhibitor
+Conestat alfa	inhibitor	2161	F12	Inhibitor
+Thrombin	activator	2162	F13A1	Activator
+Anti-inhibitor coagulant complex	agonist	2162	F13A1	Activator
+Prothrombin	agonist	2162	F13A1	Activator
+Human thrombin	activator	2162	F13A1	Activator
+Thrombin	activator	2165	F13B	Activator
+Human thrombin	activator	2165	F13B	Activator
+Catridecacog	unknown	2165	F13B	other
+Icosapent	agonist	2173	FABP7	Activator
+Benzylpenicilloyl polylysine	agonist	2205	FCER1A	Activator
+Omalizumab	inhibitor	2205	FCER1A	Inhibitor
+Omalizumab	inhibitor	2206	MS4A2	Inhibitor
+Benzylpenicilloyl polylysine	agonist	2207	FCER1G	Activator
+Trastuzumab deruxtecan	antibody	2209	FCGR1A	Modulator
+Human immunoglobulin G	antagonist	2209	FCGR1A	Inhibitor
+Human immunoglobulin G	antagonist	2212	FCGR2A	Inhibitor
+Antithymocyte immunoglobulin (rabbit)	unknown	2213	FCGR2B	other
+Human immunoglobulin G	antagonist	2213	FCGR2B	Inhibitor
+Amivantamab	inducer	2214	FCGR3A	Activator
+Benralizumab	binding	2214	FCGR3A	Ligand
+Human immunoglobulin G	antagonist	2214	FCGR3A	Inhibitor
+Human immunoglobulin G	antagonist	2215	FCGR3B	Inhibitor
+Efgartigimod alfa	binder	2217	FCGRT	Ligand
+Zoledronic acid	inhibitor	2224	FDPS	Inhibitor
+Ibandronate	inhibitor	2224	FDPS	Inhibitor
+Pamidronic acid	inhibitor	2224	FDPS	Inhibitor
+Alendronic acid	inhibitor	2224	FDPS	Inhibitor
+Risedronic acid	inhibitor	2224	FDPS	Inhibitor
+Alteplase	binder	2243	FGA	Ligand
+Anti-inhibitor coagulant complex	cleavage	2243	FGA	Inhibitor
+Prothrombin	cleavage	2243	FGA	Inhibitor
+Thrombin	activator	2243	FGA	Activator
+Human thrombin	activator	2243	FGA	Activator
+Reteplase	unknown	2243	FGA	other
+Thrombin alfa	activator	2243	FGA	Activator
+Tenecteplase	unknown	2243	FGA	other
+Anistreplase	unknown	2243	FGA	other
+Prothrombin	cleavage	2244	FGB	Inhibitor
+Human thrombin	activator	2244	FGB	Activator
+Thrombin	activator	2244	FGB	Activator
+Thrombin alfa	activator	2244	FGB	Activator
+Anti-inhibitor coagulant complex	cleavage	2244	FGB	Inhibitor
+Pentosan polysulfate	antagonist	2246	FGF1	Inhibitor
+Foreskin keratinocyte (neonatal)	agonist	2246	FGF1	Activator
+Sucralfate	agonist|inducer	2247	FGF2	Activator
+Pentosan polysulfate	antagonist	2247	FGF2	Inhibitor
+Pemigatinib	inhibitor	2260	FGFR1	Inhibitor
+Erdafitinib	inhibitor	2260	FGFR1	Inhibitor
+Lenvatinib	inhibitor	2260	FGFR1	Inhibitor
+Regorafenib	inhibitor	2260	FGFR1	Inhibitor
+Sorafenib	inhibitor	2260	FGFR1	Inhibitor
+Futibatinib	inhibitor	2260	FGFR1	Inhibitor
+Nintedanib	inhibitor	2260	FGFR1	Inhibitor
+Infigratinib	inhibitor	2260	FGFR1	Inhibitor
+Infigratinib	inhibitor	2261	FGFR3	Inhibitor
+Erdafitinib	inhibitor	2261	FGFR3	Inhibitor
+Lenvatinib	inhibitor	2261	FGFR3	Inhibitor
+Futibatinib	inhibitor	2261	FGFR3	Inhibitor
+Pemigatinib	inhibitor	2261	FGFR3	Inhibitor
+Nintedanib	inhibitor	2261	FGFR3	Inhibitor
+Infigratinib	inhibitor	2263	FGFR2	Inhibitor
+Regorafenib	inhibitor	2263	FGFR2	Inhibitor
+Futibatinib	inhibitor	2263	FGFR2	Inhibitor
+Nintedanib	inhibitor	2263	FGFR2	Inhibitor
+Lenvatinib	inhibitor	2263	FGFR2	Inhibitor
+Pemigatinib	inhibitor	2263	FGFR2	Inhibitor
+Erdafitinib	inhibitor	2263	FGFR2	Inhibitor
+Palifermin	agonist|binder	2263	FGFR2	Activator
+Lenvatinib	inhibitor	2264	FGFR4	Inhibitor
+Infigratinib	inhibitor	2264	FGFR4	Inhibitor
+Erdafitinib	inhibitor	2264	FGFR4	Inhibitor
+Futibatinib	inhibitor	2264	FGFR4	Inhibitor
+Alteplase	binder	2266	FGG	Ligand
+Thrombin	activator	2266	FGG	Activator
+Human thrombin	activator	2266	FGG	Activator
+Thrombin alfa	activator	2266	FGG	Activator
+Tacrolimus	inhibitor	2280	FKBP1A	Inhibitor
+Sunitinib	inhibitor	2321	FLT1	Inhibitor
+Axitinib	inhibitor	2321	FLT1	Inhibitor
+Regorafenib	inhibitor	2321	FLT1	Inhibitor
+Pazopanib	inhibitor	2321	FLT1	Inhibitor
+Sorafenib	inhibitor	2321	FLT1	Inhibitor
+Nintedanib	inhibitor	2321	FLT1	Inhibitor
+Lenvatinib	inhibitor	2321	FLT1	Inhibitor
+Tivozanib	inhibitor	2321	FLT1	Inhibitor
+Foreskin fibroblast (neonatal)	agonist	2321	FLT1	Activator
+Sorafenib	antagonist	2322	FLT3	Inhibitor
+Midostaurin	antagonist|inhibitor	2322	FLT3	Activator
+Gilteritinib	inhibitor	2322	FLT3	Inhibitor
+Pacritinib	inhibitor	2322	FLT3	Inhibitor
+Sunitinib	inhibitor	2322	FLT3	Inhibitor
+Fedratinib	inhibitor	2322	FLT3	Inhibitor
+Lenvatinib	inhibitor	2324	FLT4	Inhibitor
+Sorafenib	antagonist	2324	FLT4	Inhibitor
+Axitinib	inhibitor	2324	FLT4	Inhibitor
+Tivozanib	inhibitor	2324	FLT4	Inhibitor
+Regorafenib	inhibitor	2324	FLT4	Inhibitor
+Pazopanib	unknown	2324	FLT4	other
+Nintedanib	inhibitor	2324	FLT4	Inhibitor
+Sunitinib	inhibitor	2324	FLT4	Inhibitor
+Ocriplasmin	cleavage	2335	FN1	Inhibitor
+Lonafarnib	inhibitor	2339	FNTA	Inhibitor
+Lonafarnib	inhibitor	2342	FNTB	Inhibitor
+Capromab pendetide	unknown	2346	FOLH1	other
+Piflufolastat F 18	binder	2346	FOLH1	Ligand
+Mirvetuximab soravtansine	binder	2348	FOLR1	Ligand
+Pafolacianine	ligand	2348	FOLR1	Ligand
+Pralatrexate	substrate	2356	FPGS	Activator
+Regorafenib	inhibitor	2444	FRK	Inhibitor
+Sirolimus	inhibitor	2475	MTOR	Inhibitor
+Temsirolimus	inhibitor	2475	MTOR	Inhibitor
+Pimecrolimus	potentiator	2475	MTOR	Activator
+Everolimus	inhibitor	2475	MTOR	Inhibitor
+Corifollitropin alfa	agonist	2492	FSHR	Activator
+Urofollitropin	agonist	2492	FSHR	Activator
+Follitropin	agonist	2492	FSHR	Activator
+Choriogonadotropin alfa	binder	2492	FSHR	Ligand
+Menotropins	binder	2492	FSHR	Ligand
+Iron Dextran	other	2495	FTH1	other
+Ferric pyrophosphate citrate	binder	2495	FTH1	Ligand
+Iron Dextran	other	2512	FTL	other
+Sodium ferric gluconate complex	binding	2512	FTL	Ligand
+Ferric pyrophosphate citrate	binder	2512	FTL	Ligand
+Oxetacaine	inhibition of synthesis	2520	GAST	Inhibitor
+Miglitol	antagonist	2548	GAA	Inhibitor
+Taurine	agonist	2550	GABBR1	Activator
+Acamprosate	inhibitor	2550	GABBR1	Inhibitor
+Methohexital	antagonist	2554	GABRA1	Inhibitor
+Butalbital	potentiator	2554	GABRA1	Activator
+Meprobamate	agonist	2554	GABRA1	Activator
+Etomidate	agonist	2554	GABRA1	Activator
+Isoflurane	agonist	2554	GABRA1	Activator
+Secobarbital	potentiator	2554	GABRA1	Activator
+Zaleplon	potentiator	2554	GABRA1	Activator
+Ethanol	agonist	2554	GABRA1	Activator
+Primidone	potentiator	2554	GABRA1	Activator
+Pentobarbital	potentiator	2554	GABRA1	Activator
+Zolpidem	agonist	2554	GABRA1	Activator
+Butobarbital	potentiator	2554	GABRA1	Activator
+Talbutal	potentiator	2554	GABRA1	Activator
+Quinidine barbiturate	potentiator	2554	GABRA1	Activator
+Flumazenil	antagonist	2554	GABRA1	Inhibitor
+Methylphenobarbital	potentiator	2554	GABRA1	Activator
+Phenobarbital	potentiator	2554	GABRA1	Activator
+Thiamylal	agonist	2554	GABRA1	Activator
+Zopiclone	potentiator	2554	GABRA1	Activator
+Eszopiclone	potentiator	2554	GABRA1	Activator
+Glutethimide	agonist	2554	GABRA1	Activator
+Topiramate	agonist	2554	GABRA1	Activator
+Thiopental	potentiator	2554	GABRA1	Activator
+Amobarbital	potentiator	2554	GABRA1	Activator
+Methylphenobarbital	potentiator	2555	GABRA2	Activator
+Meprobamate	agonist	2555	GABRA2	Activator
+Butobarbital	potentiator	2555	GABRA2	Activator
+Thiopental	potentiator	2555	GABRA2	Activator
+Quinidine barbiturate	potentiator	2555	GABRA2	Activator
+Secobarbital	potentiator	2555	GABRA2	Activator
+Primidone	potentiator	2555	GABRA2	Activator
+Zopiclone	potentiator	2555	GABRA2	Activator
+Amobarbital	potentiator	2555	GABRA2	Activator
+Butalbital	potentiator	2555	GABRA2	Activator
+Talbutal	potentiator	2555	GABRA2	Activator
+Pentobarbital	potentiator	2555	GABRA2	Activator
+Thiopental	potentiator	2556	GABRA3	Activator
+Methylphenobarbital	potentiator	2556	GABRA3	Activator
+Secobarbital	potentiator	2556	GABRA3	Activator
+Butalbital	potentiator	2556	GABRA3	Activator
+Meprobamate	agonist	2556	GABRA3	Activator
+Butobarbital	potentiator	2556	GABRA3	Activator
+Amobarbital	potentiator	2556	GABRA3	Activator
+Pentobarbital	potentiator	2556	GABRA3	Activator
+Primidone	potentiator	2556	GABRA3	Activator
+Zopiclone	potentiator	2556	GABRA3	Activator
+Talbutal	potentiator	2556	GABRA3	Activator
+Butalbital	potentiator	2557	GABRA4	Activator
+Amobarbital	potentiator	2557	GABRA4	Activator
+Meprobamate	agonist	2557	GABRA4	Activator
+Methylphenobarbital	potentiator	2557	GABRA4	Activator
+Pentobarbital	potentiator	2557	GABRA4	Activator
+Secobarbital	potentiator	2557	GABRA4	Activator
+Primidone	potentiator	2557	GABRA4	Activator
+Butobarbital	potentiator	2557	GABRA4	Activator
+Thiopental	potentiator	2557	GABRA4	Activator
+Talbutal	potentiator	2557	GABRA4	Activator
+Flumazenil	antagonist	2558	GABRA5	Inhibitor
+Butalbital	potentiator	2558	GABRA5	Activator
+Secobarbital	potentiator	2558	GABRA5	Activator
+Meprobamate	agonist	2558	GABRA5	Activator
+Primidone	potentiator	2558	GABRA5	Activator
+Butobarbital	potentiator	2558	GABRA5	Activator
+Methylphenobarbital	potentiator	2558	GABRA5	Activator
+Pentobarbital	potentiator	2558	GABRA5	Activator
+Talbutal	potentiator	2558	GABRA5	Activator
+Thiopental	potentiator	2558	GABRA5	Activator
+Zopiclone	potentiator	2558	GABRA5	Activator
+Amobarbital	potentiator	2558	GABRA5	Activator
+Methylphenobarbital	potentiator	2559	GABRA6	Activator
+Butalbital	potentiator	2559	GABRA6	Activator
+Secobarbital	potentiator	2559	GABRA6	Activator
+Meprobamate	agonist	2559	GABRA6	Activator
+Amobarbital	potentiator	2559	GABRA6	Activator
+Butobarbital	potentiator	2559	GABRA6	Activator
+Pentobarbital	potentiator	2559	GABRA6	Activator
+Primidone	potentiator	2559	GABRA6	Activator
+Thiopental	potentiator	2559	GABRA6	Activator
+Talbutal	potentiator	2559	GABRA6	Activator
+gamma-Hydroxybutyric acid	agonist	2560	GABRB1	Activator
+Propofol	potentiator	2561	GABRB2	Activator
+Fospropofol	potentiator	2561	GABRB2	Activator
+Piperazine	agonist	2562	GABRB3	Activator
+Propofol	potentiator	2562	GABRB3	Activator
+Fospropofol	potentiator	2562	GABRB3	Activator
+Flumazenil	antagonist	2566	GABRG2	Inhibitor
+Creatine	product of	2593	GAMT	other
+Miglitol	antagonist	2595	GANC	Inhibitor
+Pemetrexed	inhibitor	2618	GART	Inhibitor
+Glucagon	agonist	2642	GCGR	Activator
+Dasiglucagon	agonist	2642	GCGR	Activator
+Menadione	cofactor	2677	GGCX	other
+Phylloquinone	substrate|inducer|cofactor	2677	GGCX	Activator
+Anisindione	inhibitor	2677	GGCX	Inhibitor
+Pegvisomant	antagonist	2690	GHR	Inhibitor
+Lonapegsomatropin	ligand	2690	GHR	Ligand
+Somatotropin	ligand	2690	GHR	Ligand
+Tesamorelin	binder	2692	GHRHR	Ligand
+Tirzepatide	agonist	2695	GIP	Activator
+Tirzepatide	agonist	2740	GLP1R	Activator
+Dulaglutide	agonist	2740	GLP1R	Activator
+Albiglutide	agonist	2740	GLP1R	Activator
+Liraglutide	agonist	2740	GLP1R	Activator
+Semaglutide	agonist	2740	GLP1R	Activator
+Lixisenatide	agonist	2740	GLP1R	Activator
+Exenatide	agonist	2740	GLP1R	Activator
+Ethanol	agonist	2741	GLRA1	Activator
+Desflurane	agonist	2741	GLRA1	Activator
+Isoflurane	agonist	2741	GLRA1	Activator
+Enflurane	potentiator	2741	GLRA1	Activator
+Sevoflurane	agonist	2741	GLRA1	Activator
+Taurine	agonist	2742	GLRA2	Activator
+Ethanol	agonist	2742	GLRA2	Activator
+Nafarelin	agonist	2798	GNRHR	Activator
+Leuprolide	agonist	2798	GNRHR	Activator
+Buserelin	unknown	2798	GNRHR	other
+Cetrorelix	antagonist	2798	GNRHR	Inhibitor
+Gonadorelin	agonist	2798	GNRHR	Activator
+Relugolix	antagonist	2798	GNRHR	Inhibitor
+Triptorelin	agonist	2798	GNRHR	Activator
+Ganirelix	antagonist	2798	GNRHR	Inhibitor
+Degarelix	antagonist	2798	GNRHR	Inhibitor
+Goserelin	agonist	2798	GNRHR	Activator
+Danazol	negative modulator	2798	GNRHR	Inhibitor
+Linzagolix	antagonist	2798	GNRHR	Inhibitor
+Icosapent	agonist	2864	FFAR1	Activator
+Sevoflurane	antagonist	2890	GRIA1	Inhibitor
+Isoflurane	antagonist	2890	GRIA1	Inhibitor
+Perampanel	antagonist	2890	GRIA1	Inhibitor
+Desflurane	antagonist	2890	GRIA1	Inhibitor
+Orphenadrine	antagonist	2902	GRIN1	Inhibitor
+Felbamate	antagonist	2903	GRIN2A	Inhibitor
+Lumateperone	unknown	2904	GRIN2B	other
+Taurine	inhibitor	2904	GRIN2B	Inhibitor
+Felbamate	antagonist	2904	GRIN2B	Inhibitor
+Orphenadrine	antagonist	2906	GRIN2D	Inhibitor
+Prednicarbate	agonist	2908	NR3C1	Activator
+Flumethasone	agonist	2908	NR3C1	Activator
+Amcinonide	agonist	2908	NR3C1	Activator
+Mometasone furoate	unknown	2908	NR3C1	other
+Ciclesonide	agonist	2908	NR3C1	Activator
+Dexamethasone acetate	agonist	2908	NR3C1	Activator
+Ulobetasol	agonist	2908	NR3C1	Activator
+Hydrocortisone phosphate	unknown	2908	NR3C1	other
+Flunisolide	agonist	2908	NR3C1	Activator
+Prednisone	agonist	2908	NR3C1	Activator
+Betamethasone	agonist	2908	NR3C1	Activator
+Hydrocortisone valerate	unknown	2908	NR3C1	other
+Hydrocortisone	agonist	2908	NR3C1	Activator
+Clocortolone	agonist	2908	NR3C1	Activator
+Beclomethasone dipropionate	agonist	2908	NR3C1	Activator
+Hydrocortisone butyrate	unknown	2908	NR3C1	other
+Clobetasol propionate	agonist	2908	NR3C1	Activator
+Fluticasone propionate	agonist	2908	NR3C1	Activator
+Hydrocortamate	agonist	2908	NR3C1	Activator
+Desonide	agonist	2908	NR3C1	Activator
+Cortisone acetate	agonist	2908	NR3C1	Activator
+Fluocinolone acetonide	agonist	2908	NR3C1	Activator
+Desoximetasone	agonist	2908	NR3C1	Activator
+Clobetasone	agonist	2908	NR3C1	Activator
+Budesonide	agonist	2908	NR3C1	Activator
+Hydrocortisone acetate	unknown	2908	NR3C1	other
+Alclometasone	agonist	2908	NR3C1	Activator
+Fluorometholone	agonist	2908	NR3C1	Activator
+Diflorasone	agonist	2908	NR3C1	Activator
+Methylprednisolone	agonist	2908	NR3C1	Activator
+Loteprednol etabonate	agonist	2908	NR3C1	Activator
+Ulipristal	antagonist	2908	NR3C1	Inhibitor
+Medrysone	agonist	2908	NR3C1	Activator
+Fluticasone	agonist	2908	NR3C1	Activator
+Dexamethasone	agonist	2908	NR3C1	Activator
+Prednisolone	agonist	2908	NR3C1	Activator
+Mifepristone	antagonist	2908	NR3C1	Inhibitor
+Fluocinonide	agonist	2908	NR3C1	Activator
+Difluprednate	agonist	2908	NR3C1	Activator
+Hydrocortisone cypionate	unknown	2908	NR3C1	other
+Flurandrenolide	agonist	2908	NR3C1	Activator
+Rimexolone	agonist	2908	NR3C1	Activator
+Fluticasone furoate	agonist	2908	NR3C1	Activator
+Hydrocortisone probutate	unknown	2908	NR3C1	other
+Triamcinolone	agonist	2908	NR3C1	Activator
+Prednisolone acetate	agonist	2908	NR3C1	Activator
+Acamprosate	antagonist	2915	GRM5	Inhibitor
+Carmustine	inhibitor	2936	GSR	Inhibitor
+Acetylcysteine	stimulator	2937	GSS	Activator
+Isosorbide mononitrate	activator	2977	GUCY1A2	Activator
+Nitric Oxide	inducer	2977	GUCY1A2	Activator
+Riociguat	agonist|stimulator	2977	GUCY1A2	Activator
+Methylene blue	inhibitor	2977	GUCY1A2	Inhibitor
+Plecanatide	agonist	2977	GUCY1A2	Activator
+Vericiguat	stimulator	2983	GUCY1B1	Activator
+Linaclotide	agonist	2984	GUCY2C	Activator
+Omega-3-carboxylic acids	potentiator	3028	HSD17B10	Activator
+Omega-3-carboxylic acids	potentiator	3033	HADH	Activator
+Ferric pyrophosphate citrate	binder	3039	HBA1	Ligand
+Ferrous sulfate anhydrous	binder	3039	HBA1	Ligand
+Pentaerythritol tetranitrate	agonist	3039	HBA1	Activator
+Voxelotor	binder	3039	HBA1	Ligand
+Nitrous acid	oxidizer	3039	HBA1	other
+Iron Dextran	activator	3039	HBA1	Activator
+Ferric pyrophosphate citrate	binder	3040	HBA2	Ligand
+Ferrous sulfate anhydrous	binder	3040	HBA2	Ligand
+Pentaerythritol tetranitrate	agonist	3040	HBA2	Activator
+Voxelotor	binder	3040	HBA2	Ligand
+Nitrous acid	oxidizer	3040	HBA2	other
+Iron Dextran	activator	3040	HBA2	Activator
+Ferric pyrophosphate citrate	binder	3043	HBB	Ligand
+Iron Dextran	activator	3043	HBB	Activator
+Pentaerythritol tetranitrate	agonist	3043	HBB	Activator
+Nitrous acid	oxidizer	3043	HBB	other
+Sodium ferric gluconate complex	binding	3043	HBB	Ligand
+Sulodexide	agonist	3053	SERPIND1	Activator
+Lemborexant	antagonist	3061	HCRTR1	Inhibitor
+Suvorexant	antagonist	3061	HCRTR1	Inhibitor
+Daridorexant	antagonist	3061	HCRTR1	Inhibitor
+Daridorexant	antagonist	3062	HCRTR2	Inhibitor
+Suvorexant	antagonist	3062	HCRTR2	Inhibitor
+Lemborexant	antagonist	3062	HCRTR2	Inhibitor
+Romidepsin	antagonist|inhibitor	3065	HDAC1	Activator
+Vorinostat	inhibitor	3065	HDAC1	Inhibitor
+Aminophylline	activator	3066	HDAC2	Activator
+Romidepsin	antagonist|inhibitor	3066	HDAC2	Activator
+Vorinostat	inhibitor	3066	HDAC2	Inhibitor
+Oxtriphylline	activator	3066	HDAC2	Activator
+Theophylline	activator	3066	HDAC2	Activator
+Antithymocyte immunoglobulin (rabbit)	antagonist	3140	MR1	Inhibitor
+Fluvastatin	inhibitor	3156	HMGCR	Inhibitor
+Simvastatin	inhibitor	3156	HMGCR	Inhibitor
+Lovastatin	inhibitor	3156	HMGCR	Inhibitor
+Rosuvastatin	inhibitor	3156	HMGCR	Inhibitor
+Atorvastatin	inhibitor	3156	HMGCR	Inhibitor
+Pravastatin	inhibitor	3156	HMGCR	Inhibitor
+Pitavastatin	inhibitor	3156	HMGCR	Inhibitor
+Hyaluronic acid	binder	3161	HMMR	Ligand
+Nitisinone	inhibitor	3242	HPD	Inhibitor
+Mercaptopurine	inhibitor	3251	HPRT1	Inhibitor
+Azelastine	antagonist	3269	HRH1	Inhibitor
+Levocetirizine	antagonist|inhibitor	3269	HRH1	Activator
+Mepyramine	antagonist	3269	HRH1	Inhibitor
+Bilastine	antagonist	3269	HRH1	Inhibitor
+Betahistine	agonist	3269	HRH1	Activator
+Dimetindene	antagonist	3269	HRH1	Inhibitor
+Dosulepin	antagonist	3269	HRH1	Inhibitor
+Pheniramine	inverse agonist	3269	HRH1	Modulator
+Olopatadine	antagonist	3269	HRH1	Inhibitor
+Diphenylpyraline	antagonist	3269	HRH1	Inhibitor
+Ketotifen	antagonist	3269	HRH1	Inhibitor
+Doxylamine	antagonist	3269	HRH1	Inhibitor
+Cetirizine	antagonist	3269	HRH1	Inhibitor
+Histamine	agonist	3269	HRH1	Activator
+Diphenhydramine	antagonist	3269	HRH1	Inhibitor
+Phenindamine	antagonist	3269	HRH1	Inhibitor
+Alimemazine	antagonist	3269	HRH1	Inhibitor
+Dexbrompheniramine	antagonist	3269	HRH1	Inhibitor
+Cyclizine	antagonist	3269	HRH1	Inhibitor
+Fexofenadine	antagonist	3269	HRH1	Inhibitor
+Orphenadrine	antagonist	3269	HRH1	Inhibitor
+Chlorpromazine	antagonist	3269	HRH1	Inhibitor
+Epinastine	antagonist	3269	HRH1	Inhibitor
+Methdilazine	antagonist	3269	HRH1	Inhibitor
+Levocabastine	antagonist	3269	HRH1	Inhibitor
+Emedastine	antagonist	3269	HRH1	Inhibitor
+Cyproheptadine	antagonist	3269	HRH1	Inhibitor
+Chlorcyclizine	antagonist	3269	HRH1	Inhibitor
+Bromodiphenhydramine	antagonist	3269	HRH1	Inhibitor
+Dimenhydrinate	antagonist	3269	HRH1	Inhibitor
+Desloratadine	antagonist	3269	HRH1	Inhibitor
+Dexchlorpheniramine maleate	antagonist	3269	HRH1	Inhibitor
+Buclizine	antagonist	3269	HRH1	Inhibitor
+Promethazine	antagonist	3269	HRH1	Inhibitor
+Triprolidine	antagonist	3269	HRH1	Inhibitor
+Antazoline	antagonist	3269	HRH1	Inhibitor
+Brompheniramine	antagonist	3269	HRH1	Inhibitor
+Cinnarizine	antagonist	3269	HRH1	Inhibitor
+Propiomazine	antagonist	3269	HRH1	Inhibitor
+Repaglinide	antagonist	3269	HRH1	Inhibitor
+Bepotastine	antagonist	3269	HRH1	Inhibitor
+Carbinoxamine	antagonist	3269	HRH1	Inhibitor
+Chlorpheniramine	antagonist	3269	HRH1	Inhibitor
+Acrivastine	antagonist	3269	HRH1	Inhibitor
+Lorpiprazole	antagonist	3269	HRH1	Inhibitor
+Thonzylamine	antagonist	3269	HRH1	Inhibitor
+Azatadine	antagonist	3269	HRH1	Inhibitor
+Hydroxyzine	inverse agonist	3269	HRH1	Modulator
+Loratadine	antagonist	3269	HRH1	Inhibitor
+Rupatadine	antagonist	3269	HRH1	Inhibitor
+Tripelennamine	antagonist	3269	HRH1	Inhibitor
+Clemastine	antagonist	3269	HRH1	Inhibitor
+Doxepin	antagonist	3269	HRH1	Inhibitor
+Meclizine	antagonist	3269	HRH1	Inhibitor
+Methantheline	antagonist	3274	HRH2	Inhibitor
+Betazole	agonist	3274	HRH2	Activator
+Cimetidine	antagonist	3274	HRH2	Inhibitor
+Epinastine	antagonist	3274	HRH2	Inhibitor
+Histamine	agonist	3274	HRH2	Activator
+Doxepin	antagonist	3274	HRH2	Inhibitor
+Famotidine	antagonist	3274	HRH2	Inhibitor
+Nizatidine	antagonist	3274	HRH2	Inhibitor
+Glycyrrhizic acid	antagonist	3290	HSD11B1	Inhibitor
+Buspirone	partial agonist	3350	HTR1A	Modulator
+Alverine	antagonist	3350	HTR1A	Inhibitor
+Methysergide	agonist	3350	HTR1A	Activator
+Trazodone	antagonist|partial agonist	3350	HTR1A	Activator
+Naratriptan	agonist	3350	HTR1A	Activator
+Flibanserin	agonist	3350	HTR1A	Activator
+Sumatriptan	agonist	3350	HTR1A	Activator
+Aripiprazole lauroxil	partial agonist	3350	HTR1A	Modulator
+Vortioxetine	agonist	3350	HTR1A	Activator
+Ziprasidone	agonist	3350	HTR1A	Activator
+Penbutolol	antagonist	3350	HTR1A	Inhibitor
+Dosulepin	antagonist	3350	HTR1A	Inhibitor
+Pipotiazine	antagonist	3350	HTR1A	Inhibitor
+Brexpiprazole	agonist|partial agonist	3350	HTR1A	Activator
+Chlorpromazine	antagonist	3350	HTR1A	Inhibitor
+Vortioxetine	partial agonist	3351	HTR1B	Modulator
+Zolmitriptan	agonist	3351	HTR1B	Activator
+Naratriptan	agonist	3351	HTR1B	Activator
+Rizatriptan	agonist	3351	HTR1B	Activator
+Eletriptan	agonist	3351	HTR1B	Activator
+Frovatriptan	agonist	3351	HTR1B	Activator
+Sumatriptan	agonist	3351	HTR1B	Activator
+Dihydroergotamine	agonist	3351	HTR1B	Activator
+Ergotamine	agonist	3351	HTR1B	Activator
+Naratriptan	agonist	3352	HTR1D	Activator
+Frovatriptan	agonist	3352	HTR1D	Activator
+Ziprasidone	antagonist	3352	HTR1D	Inhibitor
+Dihydroergotamine	agonist	3352	HTR1D	Activator
+Eletriptan	agonist	3352	HTR1D	Activator
+Sumatriptan	agonist	3352	HTR1D	Activator
+Ergotamine	agonist	3352	HTR1D	Activator
+Zolmitriptan	agonist	3352	HTR1D	Activator
+Rizatriptan	agonist	3352	HTR1D	Activator
+Eletriptan	agonist	3355	HTR1F	Activator
+Naratriptan	agonist	3355	HTR1F	Activator
+Dihydroergotamine	agonist	3355	HTR1F	Activator
+Sumatriptan	agonist	3355	HTR1F	Activator
+Zolmitriptan	agonist	3355	HTR1F	Activator
+Lasmiditan	agonist	3355	HTR1F	Activator
+Rizatriptan	agonist	3355	HTR1F	Activator
+Dosulepin	antagonist	3356	HTR2A	Inhibitor
+Mesoridazine	antagonist	3356	HTR2A	Inhibitor
+Amisulpride	antagonist	3356	HTR2A	Inhibitor
+Nortriptyline	antagonist	3356	HTR2A	Inhibitor
+Ergotamine	agonist	3356	HTR2A	Activator
+Minaprine	antagonist	3356	HTR2A	Inhibitor
+Aripiprazole lauroxil	antagonist	3356	HTR2A	Inhibitor
+Lurasidone	antagonist	3356	HTR2A	Inhibitor
+Quetiapine	antagonist	3356	HTR2A	Inhibitor
+Methysergide	antagonist	3356	HTR2A	Inhibitor
+Desipramine	antagonist	3356	HTR2A	Inhibitor
+Risperidone	antagonist	3356	HTR2A	Inhibitor
+Cyclobenzaprine	antagonist	3356	HTR2A	Inhibitor
+Cyproheptadine	antagonist	3356	HTR2A	Inhibitor
+Lorpiprazole	antagonist	3356	HTR2A	Inhibitor
+Aripiprazole	antagonist|partial agonist	3356	HTR2A	Activator
+Mirtazapine	antagonist	3356	HTR2A	Inhibitor
+Ziprasidone	antagonist	3356	HTR2A	Inhibitor
+Chlorpromazine	antagonist	3356	HTR2A	Inhibitor
+Clomipramine	antagonist	3356	HTR2A	Inhibitor
+Paliperidone	antagonist	3356	HTR2A	Inhibitor
+Amitriptyline	antagonist	3356	HTR2A	Inhibitor
+Olanzapine	antagonist	3356	HTR2A	Inhibitor
+Lumateperone	antagonist	3356	HTR2A	Inhibitor
+Trazodone	antagonist	3356	HTR2A	Inhibitor
+Clozapine	antagonist	3356	HTR2A	Inhibitor
+Flibanserin	antagonist	3356	HTR2A	Inhibitor
+Brexpiprazole	antagonist	3356	HTR2A	Inhibitor
+Pipotiazine	antagonist	3356	HTR2A	Inhibitor
+Iloperidone	antagonist	3356	HTR2A	Inhibitor
+Lisuride	agonist	3356	HTR2A	Activator
+Pimavanserin	inverse agonist	3356	HTR2A	Modulator
+Asenapine	antagonist	3356	HTR2A	Inhibitor
+Loxapine	antagonist	3356	HTR2A	Inhibitor
+Clomipramine	antagonist	3357	HTR2B	Inhibitor
+Triflupromazine	antagonist	3357	HTR2B	Inhibitor
+Methysergide	antagonist	3357	HTR2B	Inhibitor
+Minaprine	antagonist	3357	HTR2B	Inhibitor
+Clomipramine	antagonist	3358	HTR2C	Inhibitor
+Methysergide	antagonist	3358	HTR2C	Inhibitor
+Ziprasidone	antagonist	3358	HTR2C	Inhibitor
+Paliperidone	antagonist	3358	HTR2C	Inhibitor
+Trazodone	agonist	3358	HTR2C	Activator
+Cyproheptadine	antagonist	3358	HTR2C	Inhibitor
+Lisuride	agonist	3358	HTR2C	Activator
+Agomelatine	antagonist	3358	HTR2C	Inhibitor
+Lorpiprazole	antagonist	3358	HTR2C	Inhibitor
+Minaprine	antagonist	3358	HTR2C	Inhibitor
+Haloperidol	unknown	3358	HTR2C	other
+Ketamine	potentiator	3359	HTR3A	Activator
+Vortioxetine	antagonist	3359	HTR3A	Inhibitor
+Granisetron	antagonist	3359	HTR3A	Inhibitor
+Palonosetron	antagonist	3359	HTR3A	Inhibitor
+Tubocurarine	antagonist	3359	HTR3A	Inhibitor
+Procaine	antagonist	3359	HTR3A	Inhibitor
+Tropisetron	antagonist	3359	HTR3A	Inhibitor
+Metoclopramide	antagonist	3359	HTR3A	Inhibitor
+Dolasetron	antagonist	3359	HTR3A	Inhibitor
+Metoclopramide	agonist	3360	HTR4	Activator
+Prucalopride	agonist	3360	HTR4	Activator
+Amisulpride	antagonist	3363	HTR7	Inhibitor
+Vortioxetine	antagonist	3363	HTR7	Inhibitor
+Methysergide	antagonist	3363	HTR7	Inhibitor
+Hyaluronic acid	inhibitor|binder	3383	ICAM1	Inhibitor
+Bacitracin	inhibitor	3416	IDE	Inhibitor
+Olutasidenib	inhibitor	3417	IDH1	Inhibitor
+Ivosidenib	inhibitor	3417	IDH1	Inhibitor
+Enasidenib	inhibitor	3418	IDH2	Inhibitor
+Interferon beta-1a	agonist	3454	IFNAR1	Activator
+Peginterferon alfa-2a	agonist	3454	IFNAR1	Activator
+Anifrolumab	inhibitor	3454	IFNAR1	Inhibitor
+Interferon beta-1b	agonist	3454	IFNAR1	Activator
+Interferon alfa-n3	agonist	3454	IFNAR1	Activator
+Interferon alfa-n1	agonist	3454	IFNAR1	Activator
+Peginterferon alfa-2b	agonist	3454	IFNAR1	Activator
+Interferon alfa-2b	binder	3454	IFNAR1	Ligand
+Interferon alfacon-1	binder	3454	IFNAR1	Ligand
+Human interferon beta	binder	3454	IFNAR1	Ligand
+Interferon alfa-2a	unknown	3454	IFNAR1	other
+Interferon alfa-2b	binder	3455	IFNAR2	Ligand
+Interferon beta-1a	unknown	3455	IFNAR2	other
+Peginterferon alfa-2a	agonist	3455	IFNAR2	Activator
+Interferon alfa-n1	agonist	3455	IFNAR2	Activator
+Interferon alfacon-1	binder	3455	IFNAR2	Ligand
+Interferon alfa-2a	unknown	3455	IFNAR2	other
+Interferon beta-1b	agonist	3455	IFNAR2	Activator
+Nylidrin	agonist	3455	IFNAR2	Activator
+Interferon alfa-n3	agonist	3455	IFNAR2	Activator
+Peginterferon alfa-2b	agonist	3455	IFNAR2	Activator
+Emapalumab	neutralizer	3458	IFNG	other
+Foreskin keratinocyte (neonatal)	agonist	3458	IFNG	Activator
+Olsalazine	unknown	3458	IFNG	other
+Interferon gamma-1b	binder	3459	IFNGR1	Ligand
+Interferon gamma-1b	binder	3460	IFNGR2	Ligand
+Mecasermin	agonist	3480	IGF1R	Activator
+Mecasermin rinfabate	agonist	3480	IGF1R	Activator
+Arsenic trioxide	inducer	3551	IKBKB	Activator
+Auranofin	inhibitor	3551	IKBKB	Inhibitor
+Gallium nitrate	antagonist	3553	IL1B	Inhibitor
+Foreskin keratinocyte (neonatal)	agonist	3553	IL1B	Activator
+Canakinumab	binder	3553	IL1B	Ligand
+Anakinra	antagonist	3554	IL1R1	Inhibitor
+Basiliximab	antibody	3559	IL2RA	Modulator
+Denileukin diftitox	binder	3559	IL2RA	Ligand
+Aldesleukin	agonist|modulator	3559	IL2RA	Activator
+Aldesleukin	agonist|modulator	3560	IL2RB	Activator
+Denileukin diftitox	agonist	3560	IL2RB	Activator
+Aldesleukin	agonist	3561	IL2RG	Activator
+Tagraxofusp	ligand	3563	IL3RA	Ligand
+Sargramostim	agonist	3563	IL3RA	Activator
+Dupilumab	inhibitor|antibody	3565	IL4	Inhibitor
+Dupilumab	inhibitor|antibody	3566	IL4R	Inhibitor
+Reslizumab	antagonist|regulator	3567	IL5	Activator
+Mepolizumab	antagonist|regulator	3567	IL5	Activator
+Benralizumab	antibody	3568	IL5RA	Modulator
+Foreskin keratinocyte (neonatal)	agonist	3569	IL6	Activator
+Siltuximab	antagonist|antibody	3569	IL6	Activator
+Tocilizumab	inhibitor|antibody	3570	IL6R	Inhibitor
+Satralizumab	binder|antibody	3570	IL6R	Inhibitor
+Sarilumab	antagonist|antibody	3570	IL6R	Activator
+Oprelvekin	agonist	3590	IL11RA	Activator
+Ustekinumab	inhibitor	3593	IL12B	Inhibitor
+Dupilumab	inhibitor|antibody	3596	IL13	Inhibitor
+Tralokinumab	antibody	3596	IL13	Modulator
+Bimekizumab	antibody	3605	IL17A	Modulator
+Secukinumab	inhibitor	3605	IL17A	Inhibitor
+Ixekizumab	antibody	3605	IL17A	Modulator
+Ribavirin	inhibitor	3614	IMPDH1	Inhibitor
+Mycophenolic acid	inhibitor	3614	IMPDH1	Inhibitor
+Mycophenolate mofetil	inhibitor|inducer	3614	IMPDH1	Activator
+Mycophenolate mofetil	inhibitor	3615	IMPDH2	Inhibitor
+Mycophenolic acid	inhibitor	3615	IMPDH2	Inhibitor
+Insulin pork	binder	3643	INSR	Ligand
+Insulin beef	agonist	3643	INSR	Activator
+Insulin degludec	agonist	3643	INSR	Activator
+Insulin detemir	agonist	3643	INSR	Activator
+Insulin lispro	agonist	3643	INSR	Activator
+Chromic chloride	activator	3643	INSR	Activator
+Insulin glargine	agonist	3643	INSR	Activator
+Insulin glulisine	agonist	3643	INSR	Activator
+Insulin human	agonist	3643	INSR	Activator
+Insulin aspart	agonist	3643	INSR	Activator
+Tirofiban	antagonist	3674	ITGA2B	Inhibitor
+Abciximab	antagonist	3674	ITGA2B	Inhibitor
+Natalizumab	antibody	3676	ITGA4	Modulator
+Vedolizumab	antibody	3676	ITGA4	Modulator
+Lifitegrast	antagonist	3683	ITGAL	Inhibitor
+Antithymocyte immunoglobulin (rabbit)	unknown	3683	ITGAL	other
+Levothyroxine	unknown	3685	ITGAV	other
+Antithymocyte immunoglobulin (rabbit)	unknown	3685	ITGAV	other
+Antithymocyte immunoglobulin (rabbit)	unknown	3688	ITGB1	other
+Tirofiban	antagonist	3690	ITGB3	Inhibitor
+Abciximab	antagonist	3690	ITGB3	Inhibitor
+Eptifibatide	unknown	3690	ITGB3	other
+Antithymocyte immunoglobulin (rabbit)	unknown	3690	ITGB3	other
+Levothyroxine	unknown	3690	ITGB3	other
+Vedolizumab	antibody	3695	ITGB7	Modulator
+Tofacitinib	inhibitor	3716	JAK1	Inhibitor
+Baricitinib	inhibitor	3716	JAK1	Inhibitor
+Abrocitinib	inhibitor	3716	JAK1	Inhibitor
+Upadacitinib	inhibitor	3716	JAK1	Inhibitor
+Ruxolitinib	inhibitor	3716	JAK1	Inhibitor
+Filgotinib	inhibitor	3716	JAK1	Inhibitor
+Pacritinib	inhibitor	3717	JAK2	Inhibitor
+Fedratinib	inhibitor	3717	JAK2	Inhibitor
+Baricitinib	inhibitor	3717	JAK2	Inhibitor
+Ruxolitinib	inhibitor	3717	JAK2	Inhibitor
+Tofacitinib	antagonist|inhibitor	3717	JAK2	Activator
+Tofacitinib	inhibitor	3718	JAK3	Inhibitor
+Ruxolitinib	inhibitor	3718	JAK3	Inhibitor
+Baricitinib	inhibitor	3718	JAK3	Inhibitor
+Adapalene	antagonist	3725	JUN	Inhibitor
+Arsenic trioxide	inducer	3725	JUN	Activator
+Isoflurane	inducer	3736	KCNA1	Activator
+Amifampridine	blocker	3736	KCNA1	Inhibitor
+Desflurane	inducer	3736	KCNA1	Activator
+Dalfampridine	antagonist	3736	KCNA1	Inhibitor
+Dalfampridine	antagonist	3737	KCNA2	Inhibitor
+Dalfampridine	antagonist	3738	KCNA3	Inhibitor
+Dalfampridine	antagonist	3739	KCNA4	Inhibitor
+Vernakalant	blocker	3741	KCNA5	Inhibitor
+Dalfampridine	antagonist	3741	KCNA5	Inhibitor
+Dalfampridine	antagonist	3742	KCNA6	Inhibitor
+Dalfampridine	antagonist	3743	KCNA7	Inhibitor
+Dalfampridine	antagonist	3744	KCNA10	Inhibitor
+Dalfampridine	antagonist	3745	KCNB1	Inhibitor
+Dalfampridine	antagonist	3746	KCNC1	Inhibitor
+Dalfampridine	antagonist	3747	KCNC2	Inhibitor
+Dalfampridine	antagonist	3748	KCNC3	Inhibitor
+Dalfampridine	antagonist	3750	KCND1	Inhibitor
+Dalfampridine	antagonist	3751	KCND2	Inhibitor
+Dronedarone	inhibitor	3752	KCND3	Inhibitor
+Vernakalant	blocker	3752	KCND3	Inhibitor
+Dalfampridine	antagonist	3752	KCND3	Inhibitor
+Ibutilide	inhibitor	3757	KCNH2	Inhibitor
+Propafenone	inhibitor	3757	KCNH2	Inhibitor
+Halofantrine	inhibitor	3757	KCNH2	Inhibitor
+Phenytoin	inhibitor	3757	KCNH2	Inhibitor
+Dronedarone	inhibitor	3757	KCNH2	Inhibitor
+Pimozide	inhibitor	3757	KCNH2	Inhibitor
+Dofetilide	inhibitor	3757	KCNH2	Inhibitor
+Sotalol	inhibitor	3757	KCNH2	Inhibitor
+Minoxidil	inducer	3758	KCNJ1	Activator
+Glymidine	unknown	3758	KCNJ1	other
+Bethanidine	inhibitor	3758	KCNJ1	Inhibitor
+Glimepiride	inhibitor	3758	KCNJ1	Inhibitor
+Dronedarone	inhibitor	3760	KCNJ3	Inhibitor
+Gliquidone	inhibitor	3764	KCNJ8	Inhibitor
+Levosimendan	inducer	3764	KCNJ8	Activator
+Thiamylal	inhibitor	3764	KCNJ8	Inhibitor
+Levosimendan	inducer	3767	KCNJ11	Activator
+Thiamylal	inhibitor	3767	KCNJ11	Inhibitor
+Glimepiride	inhibitor	3767	KCNJ11	Inhibitor
+Diazoxide	inducer	3767	KCNJ11	Activator
+Dofetilide	inhibitor	3768	KCNJ12	Inhibitor
+Dofetilide	inhibitor	3776	KCNK2	Inhibitor
+Dronedarone	inhibitor	3776	KCNK2	Inhibitor
+Doxapram	inhibitor	3777	KCNK3	Inhibitor
+Halothane	binder	3777	KCNK3	Ligand
+Hydrochlorothiazide	inhibitor	3778	KCNMA1	Inhibitor
+Bendroflumethiazide	inducer	3778	KCNMA1	Activator
+Chlorzoxazone	unknown	3778	KCNMA1	other
+Clotrimazole	inhibitor	3783	KCNN4	Inhibitor
+Dronedarone	inhibitor	3784	KCNQ1	Inhibitor
+Pazopanib	inhibitor	3791	KDR	Inhibitor
+Ramucirumab	antagonist	3791	KDR	Inhibitor
+Cabozantinib	antagonist	3791	KDR	Inhibitor
+Regorafenib	inhibitor	3791	KDR	Inhibitor
+Midostaurin	antagonist|inhibitor	3791	KDR	Activator
+Nintedanib	inhibitor	3791	KDR	Inhibitor
+Ripretinib	inhibitor	3791	KDR	Inhibitor
+Lenvatinib	inhibitor	3791	KDR	Inhibitor
+Tivozanib	inhibitor	3791	KDR	Inhibitor
+Axitinib	inhibitor	3791	KDR	Inhibitor
+Sunitinib	inhibitor	3791	KDR	Inhibitor
+Sorafenib	antagonist	3791	KDR	Inhibitor
+Lenvatinib	inhibitor	3815	KIT	Inhibitor
+Sorafenib	antagonist	3815	KIT	Inhibitor
+Tivozanib	inhibitor	3815	KIT	Inhibitor
+Pazopanib	inhibitor	3815	KIT	Inhibitor
+Regorafenib	inhibitor	3815	KIT	Inhibitor
+Imatinib	antagonist	3815	KIT	Inhibitor
+Sunitinib	inhibitor	3815	KIT	Inhibitor
+Ripretinib	inhibitor	3815	KIT	Inhibitor
+Conestat alfa	inhibitor	3818	KLKB1	Inhibitor
+Ecallantide	inhibitor	3818	KLKB1	Inhibitor
+Human C1-esterase inhibitor	inhibitor	3818	KLKB1	Inhibitor
+Lanadelumab	inhibitor|regulator	3818	KLKB1	Inhibitor
+Berotralstat	inhibitor	3818	KLKB1	Inhibitor
+Sotorasib	inhibitor	3845	KRAS	Inhibitor
+Adagrasib	inhibitor	3845	KRAS	Inhibitor
+Anthralin	antagonist	3849	KRT2	Inhibitor
+Anthralin	antagonist	3859	KRT12	Inhibitor
+Relatlimab	antibody	3902	LAG3	Modulator
+Dasatinib	multitarget	3932	LCK	Inhibitor
+Stiripentol	inhibitor	3939	LDHA	Inhibitor
+Stiripentol	inhibitor	3945	LDHB	Inhibitor
+Metreleptin	agonist	3953	LEPR	Activator
+Choriogonadotropin alfa	unknown	3973	LHCGR	other
+Chorionic Gonadotropin (Human)	ligand	3973	LHCGR	Ligand
+Goserelin	agonist	3973	LHCGR	Activator
+Menotropins	unknown	3973	LHCGR	other
+Buserelin	unknown	3973	LHCGR	other
+Lutropin alfa	agonist	3973	LHCGR	Activator
+alpha-Linolenic acid	ligand	3992	FADS1	Ligand
+Omega-3-carboxylic acids	stimulator	4023	LPL	Activator
+Tyloxapol	inhibitor	4023	LPL	Inhibitor
+Glycyrrhizic acid	inducer	4023	LPL	Activator
+Etanercept	antibody	4049	LTA	Modulator
+Sacituzumab govitecan	antibody	4070	TACSTD2	Modulator
+Alglucosidase alfa	binder	4074	M6PR	Ligand
+Isocarboxazid	inhibitor	4128	MAOA	Inhibitor
+Minaprine	inhibitor	4128	MAOA	Inhibitor
+Moclobemide	antagonist|inhibitor	4128	MAOA	Activator
+Phenelzine	antagonist	4128	MAOA	Inhibitor
+Tranylcypromine	inhibitor	4128	MAOA	Inhibitor
+Phentermine	antagonist	4128	MAOA	Inhibitor
+Selegiline	inhibitor	4129	MAOB	Inhibitor
+Rasagiline	inhibitor	4129	MAOB	Inhibitor
+Isocarboxazid	inhibitor	4129	MAOB	Inhibitor
+Tranylcypromine	inhibitor	4129	MAOB	Inhibitor
+Phentermine	antagonist	4129	MAOB	Inhibitor
+Safinamide	antagonist	4129	MAOB	Inhibitor
+Pargyline	inhibitor	4129	MAOB	Inhibitor
+Phenelzine	antagonist	4129	MAOB	Inhibitor
+Estramustine	antagonist	4130	MAP1A	Inhibitor
+Estramustine	antagonist	4133	MAP2	Inhibitor
+Docetaxel	unknown	4133	MAP2	other
+Paclitaxel	unknown	4133	MAP2	other
+Paclitaxel	unknown	4134	MAP4	other
+Docetaxel	unknown	4134	MAP4	other
+Paclitaxel	unknown	4137	MAPT	other
+Flortaucipir F-18	binder	4137	MAPT	Ligand
+Docetaxel	unknown	4137	MAPT	other
+Carbon monoxide	inhibitor	4151	MB	Inhibitor
+Afamelanotide	agonist	4157	MC1R	Activator
+Seractide acetate	agonist	4158	MC2R	Activator
+Corticotropin	agonist	4158	MC2R	Activator
+Tetracosactide	agonist	4158	MC2R	Activator
+Setmelanotide	agonist	4160	MC4R	Activator
+Binimetinib	inhibitor	4214	MAP3K1	Inhibitor
+Tepotinib	inhibitor	4233	MET	Inhibitor
+Cabozantinib	antagonist	4233	MET	Inhibitor
+Amivantamab	antagonist|antibody	4233	MET	Activator
+Crizotinib	inhibitor	4233	MET	Inhibitor
+Capmatinib	inhibitor	4233	MET	Inhibitor
+Progesterone	antagonist|agonist	4306	NR3C2	Modulator
+Eplerenone	antagonist	4306	NR3C2	Inhibitor
+Spironolactone	antagonist	4306	NR3C2	Inhibitor
+Drospirenone	antagonist	4306	NR3C2	Inhibitor
+Fludrocortisone	agonist	4306	NR3C2	Activator
+Finerenone	antagonist	4306	NR3C2	Inhibitor
+Sacubitril	antagonist|inhibitor	4311	MME	Activator
+Fosdenopterin	substrate	4338	MOCS2	Activator
+Lusutrombopag	agonist	4352	MPL	Activator
+Romiplostim	agonist	4352	MPL	Activator
+Avatrombopag	agonist	4352	MPL	Activator
+Technetium Tc-99m tilmanocept	ligand	4360	MRC1	Ligand
+Sulfinpyrazone	inhibitor	4363	ABCC1	Inhibitor
+Crizotinib	inhibitor	4486	MST1R	Inhibitor
+Desflurane	inhibitor	4535	ND1	Inhibitor
+Melatonin	agonist	4543	MTNR1A	Activator
+Tasimelteon	agonist	4543	MTNR1A	Activator
+Agomelatine	agonist	4543	MTNR1A	Activator
+Ramelteon	multitarget	4543	MTNR1A	Inhibitor
+Agomelatine	agonist	4544	MTNR1B	Activator
+Tasimelteon	agonist	4544	MTNR1B	Activator
+Ramelteon	multitarget	4544	MTNR1B	Inhibitor
+Melatonin	agonist	4544	MTNR1B	Activator
+Lomitapide	antagonist	4547	MTTP	Inhibitor
+Cyanocobalamin	cofactor	4548	MTR	other
+Hydroxocobalamin	cofactor	4548	MTR	other
+Mecobalamin	cofactor	4548	MTR	other
+Cyanocobalamin	cofactor	4594	MMUT	other
+Hydroxocobalamin	cofactor	4594	MMUT	other
+Mavacamten	inhibitory allosteric modulator	4625	MYH7	Inhibitor
+Ubidecarenone	cofactor	4731	NDUFV3	other
+Celecoxib	inhibitor	4758	NEU1	Inhibitor
+Triflusal	antagonist	4790	NFKB1	Inhibitor
+Niacin	binder	4837	NNMT	Ligand
+Methylene blue	inhibitor	4842	NOS1	Inhibitor
+Ketamine	inhibitor	4842	NOS1	Inhibitor
+Triflusal	agonist	4843	NOS2	Activator
+Sapropterin	cofactor	4846	NOS3	other
+Phentermine	inhibitor	4852	NPY	Inhibitor
+Cladribine	inducer	4860	PNP	Activator
+Nitroprusside	agonist	4881	NPR1	Activator
+Erythrityl tetranitrate	agonist	4881	NPR1	Activator
+Amyl Nitrite	agonist	4881	NPR1	Activator
+Isosorbide dinitrate	agonist	4881	NPR1	Activator
+Erythrityl tetranitrate	agonist	4882	NPR2	Activator
+Vosoritide	agonist	4882	NPR2	Activator
+Larotrectinib	inhibitor	4914	NTRK1	Inhibitor
+Regorafenib	inhibitor	4914	NTRK1	Inhibitor
+Entrectinib	inhibitor	4914	NTRK1	Inhibitor
+Larotrectinib	inhibitor	4915	NTRK2	Inhibitor
+Entrectinib	inhibitor	4916	NTRK3	Inhibitor
+Larotrectinib	inhibitor	4916	NTRK3	Inhibitor
+Regorafenib	inhibitor	4921	DDR2	Inhibitor
+Opium	agonist	4985	OPRD1	Activator
+Oxycodone	agonist	4985	OPRD1	Activator
+Butorphanol	agonist	4985	OPRD1	Activator
+Hydromorphone	partial agonist	4985	OPRD1	Modulator
+Naltrexone	antagonist	4985	OPRD1	Inhibitor
+Codeine	agonist	4985	OPRD1	Activator
+Naldemedine	antagonist	4985	OPRD1	Inhibitor
+Fentanyl	agonist	4985	OPRD1	Activator
+Methadone	agonist	4985	OPRD1	Activator
+Nalbuphine	antagonist	4985	OPRD1	Inhibitor
+Naloxone	antagonist	4985	OPRD1	Inhibitor
+Morphine	agonist	4985	OPRD1	Activator
+Eluxadoline	antagonist	4985	OPRD1	Inhibitor
+Hydrocodone	agonist	4985	OPRD1	Activator
+Dezocine	antagonist	4986	OPRK1	Inhibitor
+Nalbuphine	agonist	4986	OPRK1	Activator
+Pentazocine	agonist	4986	OPRK1	Activator
+Naltrexone	antagonist	4986	OPRK1	Inhibitor
+Butorphanol	agonist	4986	OPRK1	Activator
+Buprenorphine	antagonist	4986	OPRK1	Inhibitor
+Naloxone	antagonist	4986	OPRK1	Inhibitor
+Difelikefalin	agonist	4986	OPRK1	Activator
+Eluxadoline	agonist	4986	OPRK1	Activator
+Meperidine	unknown	4986	OPRK1	other
+Hydromorphone	agonist	4986	OPRK1	Activator
+Codeine	agonist	4986	OPRK1	Activator
+Naldemedine	antagonist	4986	OPRK1	Inhibitor
+Oxycodone	agonist	4986	OPRK1	Activator
+Opium	agonist	4986	OPRK1	Activator
+Morphine	agonist	4986	OPRK1	Activator
+Pholcodine	antagonist	4986	OPRK1	Inhibitor
+Oxycodone	agonist	4988	OPRM1	Activator
+Levorphanol	agonist	4988	OPRM1	Activator
+Remifentanil	agonist	4988	OPRM1	Activator
+Oliceridine	agonist	4988	OPRM1	Activator
+Methylnaltrexone	antagonist	4988	OPRM1	Inhibitor
+Alfentanil	agonist	4988	OPRM1	Activator
+Diamorphine	agonist	4988	OPRM1	Activator
+Naloxone	antagonist	4988	OPRM1	Inhibitor
+Hydromorphone	agonist	4988	OPRM1	Activator
+Buprenorphine	partial agonist	4988	OPRM1	Modulator
+Samidorphan	antagonist	4988	OPRM1	Inhibitor
+Levallorphan	partial agonist	4988	OPRM1	Modulator
+Loperamide	agonist	4988	OPRM1	Activator
+Oxymorphone	agonist	4988	OPRM1	Activator
+Morphine	agonist|regulator	4988	OPRM1	Activator
+Dezocine	agonist	4988	OPRM1	Activator
+Naloxegol	antagonist	4988	OPRM1	Inhibitor
+Alvimopan	antagonist	4988	OPRM1	Inhibitor
+Pholcodine	antagonist	4988	OPRM1	Inhibitor
+Hydrocodone	agonist	4988	OPRM1	Activator
+Naldemedine	antagonist	4988	OPRM1	Inhibitor
+Eluxadoline	agonist	4988	OPRM1	Activator
+Pentazocine	antagonist	4988	OPRM1	Inhibitor
+Butorphanol	antagonist	4988	OPRM1	Inhibitor
+Methadone	agonist	4988	OPRM1	Activator
+Trimebutine	agonist	4988	OPRM1	Activator
+Opium	agonist	4988	OPRM1	Activator
+Tramadol	agonist	4988	OPRM1	Activator
+Nalbuphine	antagonist	4988	OPRM1	Inhibitor
+Sufentanil	agonist	4988	OPRM1	Activator
+Codeine	agonist|regulator	4988	OPRM1	Activator
+Diphenoxylate	agonist	4988	OPRM1	Activator
+Anileridine	agonist	4988	OPRM1	Activator
+Naltrexone	antagonist	4988	OPRM1	Inhibitor
+Fentanyl	agonist	4988	OPRM1	Activator
+Tapentadol	agonist	4988	OPRM1	Activator
+Vasopressin	agonist	5021	OXTR	Activator
+Carbetocin	agonist	5021	OXTR	Activator
+Oxytocin	agonist	5021	OXTR	Activator
+Atosiban	antagonist	5021	OXTR	Inhibitor
+Eslicarbazepine	unknown	5025	P2RX4	other
+Eslicarbazepine acetate	antagonist	5025	P2RX4	Inhibitor
+Sapropterin	cofactor	5053	PAH	other
+Avelumab	inhibitor|antibody	5133	PDCD1	Inhibitor
+Atezolizumab	inhibitor	5133	PDCD1	Inhibitor
+Dostarlimab	binder|antibody	5133	PDCD1	Inhibitor
+Nivolumab	inhibitor|antibody	5133	PDCD1	Inhibitor
+Pembrolizumab	inhibitor|antibody	5133	PDCD1	Inhibitor
+Cemiplimab	inhibitor|antibody	5133	PDCD1	Inhibitor
+Cilostazol	inhibitor	5139	PDE3A	Inhibitor
+Theophylline	inhibitor	5139	PDE3A	Inhibitor
+Anagrelide	inhibitor	5139	PDE3A	Inhibitor
+Enoximone	inhibitor	5139	PDE3A	Inhibitor
+Oxtriphylline	inhibitor	5139	PDE3A	Inhibitor
+Amrinone	inhibitor	5139	PDE3A	Inhibitor
+Aminophylline	inhibitor	5139	PDE3A	Inhibitor
+Milrinone	inhibitor	5139	PDE3A	Inhibitor
+Drotaverine	inhibitor	5141	PDE4A	Inhibitor
+Theophylline	inhibitor	5141	PDE4A	Inhibitor
+Enprofylline	inhibitor	5141	PDE4A	Inhibitor
+Dipyridamole	inhibitor	5141	PDE4A	Inhibitor
+Dyphylline	inhibitor	5141	PDE4A	Inhibitor
+Oxtriphylline	inhibitor	5141	PDE4A	Inhibitor
+Enprofylline	inhibitor	5142	PDE4B	Inhibitor
+Dyphylline	inhibitor	5142	PDE4B	Inhibitor
+Papaverine	inhibitor	5142	PDE4B	Inhibitor
+Theophylline	inhibitor	5142	PDE4B	Inhibitor
+Dyphylline	inhibitor	5143	PDE4C	Inhibitor
+Dyphylline	inhibitor	5144	PDE4D	Inhibitor
+Foreskin keratinocyte (neonatal)	agonist	5156	PDGFRA	Activator
+Midostaurin	antagonist|inhibitor	5156	PDGFRA	Activator
+Regorafenib	inhibitor	5156	PDGFRA	Inhibitor
+Imatinib	antagonist	5156	PDGFRA	Inhibitor
+Pazopanib	inhibitor	5156	PDGFRA	Inhibitor
+Olaratumab	antagonist	5156	PDGFRA	Inhibitor
+Nintedanib	inhibitor	5156	PDGFRA	Inhibitor
+Sunitinib	inhibitor	5156	PDGFRA	Inhibitor
+Ripretinib	inhibitor	5156	PDGFRA	Inhibitor
+Lenvatinib	inhibitor	5156	PDGFRA	Inhibitor
+Sorafenib	antagonist	5159	PDGFRB	Inhibitor
+Midostaurin	antagonist|inhibitor	5159	PDGFRB	Activator
+Pazopanib	inhibitor	5159	PDGFRB	Inhibitor
+Becaplermin	unknown	5159	PDGFRB	other
+Nintedanib	inhibitor	5159	PDGFRB	Inhibitor
+Imatinib	antagonist	5159	PDGFRB	Inhibitor
+Ripretinib	inhibitor	5159	PDGFRB	Inhibitor
+Regorafenib	inhibitor	5159	PDGFRB	Inhibitor
+Sunitinib	inhibitor	5159	PDGFRB	Inhibitor
+Tivozanib	inhibitor	5159	PDGFRB	Inhibitor
+Dichloroacetic acid	inhibitor	5163	PDK1	Inhibitor
+Sucralfate	inhibitor	5222	PGA5	Inhibitor
+Aflibercept	binder	5228	PGF	Ligand
+Ulipristal	modulator	5241	PGR	Inhibitor
+Ethynodiol diacetate	agonist	5241	PGR	Activator
+Norelgestromin	agonist	5241	PGR	Activator
+Danazol	agonist	5241	PGR	Activator
+Mifepristone	antagonist	5241	PGR	Inhibitor
+Norgestimate	agonist	5241	PGR	Activator
+Norethisterone	agonist	5241	PGR	Activator
+Medroxyprogesterone acetate	agonist	5241	PGR	Activator
+Levonorgestrel	modulator	5241	PGR	Inhibitor
+Segesterone acetate	agonist|activator	5241	PGR	Activator
+Drospirenone	agonist	5241	PGR	Activator
+Desogestrel	agonist	5241	PGR	Activator
+Norgestrel	binder	5241	PGR	Ligand
+Dienogest	agonist	5241	PGR	Activator
+Etonogestrel	agonist	5241	PGR	Activator
+Megestrol acetate	agonist	5241	PGR	Activator
+Progesterone	agonist	5241	PGR	Activator
+Alpelisib	inhibitor	5290	PIK3CA	Inhibitor
+Copanlisib	inhibitor	5290	PIK3CA	Inhibitor
+Copanlisib	inhibitor	5293	PIK3CD	Inhibitor
+Duvelisib	inhibitor	5293	PIK3CD	Inhibitor
+Idelalisib	inhibitor	5293	PIK3CD	Inhibitor
+Duvelisib	inhibitor	5294	PIK3CG	Inhibitor
+Mitapivat	activator	5313	PKLR	Activator
+Sulfasalazine	antagonist	5319	PLA2G1B	Inhibitor
+Indomethacin	inhibitor	5320	PLA2G2A	Inhibitor
+Aminocaproic acid	antagonist	5327	PLAT	Inhibitor
+Conestat alfa	inhibitor	5327	PLAT	Inhibitor
+Human C1-esterase inhibitor	inhibitor	5327	PLAT	Inhibitor
+Alteplase	activator	5340	PLG	Activator
+Tranexamic acid	inhibitor	5340	PLG	Inhibitor
+Anistreplase	activator	5340	PLG	Activator
+Reteplase	activator	5340	PLG	Activator
+Aminocaproic acid	inhibitor	5340	PLG	Inhibitor
+Tenecteplase	activator	5340	PLG	Activator
+Streptokinase	activator	5340	PLG	Activator
+Orlistat	inhibitor	5406	PNLIP	Inhibitor
+Fludarabine	inhibitor	5422	POLA1	Inhibitor
+Cladribine	inhibitor	5422	POLA1	Inhibitor
+Clofarabine	inhibitor	5422	POLA1	Inhibitor
+Cytarabine	inhibitor	5423	POLB	Inhibitor
+Cladribine	inhibitor	5426	POLE	Inhibitor
+Cladribine	inhibitor	5427	POLE2	Inhibitor
+Gemfibrozil	agonist	5465	PPARA	Activator
+Clofibrate	agonist	5465	PPARA	Activator
+Fenofibric acid	agonist	5465	PPARA	Activator
+Doconexent	activator	5465	PPARA	Activator
+Fenofibrate	agonist	5465	PPARA	Activator
+Bezafibrate	agonist	5465	PPARA	Activator
+Soybean oil	activator	5465	PPARA	Activator
+Treprostinil	agonist	5467	PPARD	Activator
+Bezafibrate	agonist	5467	PPARD	Activator
+Icosapent	agonist	5467	PPARD	Activator
+Glycerin	agonist	5467	PPARD	Activator
+Icosapent	agonist|regulator	5468	PPARG	Activator
+Balsalazide	agonist	5468	PPARG	Activator
+Omega-3 fatty acids	ligand	5468	PPARG	Ligand
+Rosiglitazone	agonist	5468	PPARG	Activator
+Bezafibrate	agonist	5468	PPARG	Activator
+Telmisartan	partial agonist	5468	PPARG	Modulator
+Mesalazine	agonist	5468	PPARG	Activator
+Pioglitazone	agonist	5468	PPARG	Activator
+Doconexent	activator	5468	PPARG	Activator
+Cyclosporine	inhibitor|binder	5478	PPIA	Inhibitor
+Voclosporin	inhibitor	5534	PPP3R1	Inhibitor
+Cyclosporine	inhibitor	5535	PPP3R2	Inhibitor
+Voclosporin	inhibitor	5535	PPP3R2	Inhibitor
+Metformin	inducer|activator	5564	PRKAB1	Activator
+Midostaurin	antagonist|inhibitor	5578	PRKCA	Activator
+Arsenic trioxide	inducer	5594	MAPK1	Activator
+Arsenic trioxide	inducer	5595	MAPK3	Activator
+Regorafenib	inhibitor	5600	MAPK11	Inhibitor
+Cobimetinib	inhibitor	5604	MAP2K1	Inhibitor
+Trametinib	antagonist|inhibitor	5604	MAP2K1	Activator
+Selumetinib	inhibitor	5604	MAP2K1	Inhibitor
+Trametinib	antagonist|inhibitor	5605	MAP2K2	Activator
+Selumetinib	inhibitor	5605	MAP2K2	Inhibitor
+Binimetinib	inhibitor	5605	MAP2K2	Inhibitor
+Lonapegsomatropin	ligand	5618	PRLR	Ligand
+Somatotropin	ligand	5618	PRLR	Ligand
+Protein S human	cofactor	5624	PROC	other
+Sodium tetradecyl sulfate	inhibitor	5624	PROC	Inhibitor
+Sodium tetradecyl sulfate	inhibitor	5627	PROS1	Inhibitor
+Bortezomib	inhibitor	5689	PSMB1	Inhibitor
+Carfilzomib	inhibitor	5689	PSMB1	Inhibitor
+Carfilzomib	inhibitor	5690	PSMB2	Inhibitor
+Bortezomib	inhibitor	5693	PSMB5	Inhibitor
+Carfilzomib	inhibitor	5693	PSMB5	Inhibitor
+Carfilzomib	inhibitor	5696	PSMB8	Inhibitor
+Carfilzomib	inhibitor	5698	PSMB9	Inhibitor
+Carfilzomib	inhibitor	5699	PSMB10	Inhibitor
+Rupatadine	antagonist	5724	PTAFR	Inhibitor
+Treprostinil	agonist	5729	PTGDR	Activator
+Misoprostol	agonist	5731	PTGER1	Activator
+Iloprost	agonist	5731	PTGER1	Activator
+Dinoprostone	agonist	5731	PTGER1	Activator
+Alprostadil	agonist	5731	PTGER1	Activator
+Carboprost tromethamine	agonist	5731	PTGER1	Activator
+Bimatoprost	agonist	5731	PTGER1	Activator
+Omidenepag isopropyl	agonist	5732	PTGER2	Activator
+Misoprostol	agonist	5732	PTGER2	Activator
+Dinoprostone	agonist	5732	PTGER2	Activator
+Alprostadil	agonist	5732	PTGER2	Activator
+Treprostinil	agonist	5732	PTGER2	Activator
+Dinoprostone	agonist	5733	PTGER3	Activator
+Misoprostol	agonist	5733	PTGER3	Activator
+Castor oil	agonist|activator	5733	PTGER3	Activator
+Bimatoprost	agonist	5733	PTGER3	Activator
+Dinoprostone	agonist	5734	PTGER4	Activator
+Latanoprost	agonist	5737	PTGFR	Activator
+Latanoprostene bunod	agonist	5737	PTGFR	Activator
+Travoprost	agonist	5737	PTGFR	Activator
+Tafluprost	agonist	5737	PTGFR	Activator
+Bimatoprost	agonist	5737	PTGFR	Activator
+Dinoprost tromethamine	agonist	5737	PTGFR	Activator
+Iloprost	agonist	5739	PTGIR	Activator
+Treprostinil	agonist	5739	PTGIR	Activator
+Epoprostenol	agonist	5739	PTGIR	Activator
+Selexipag	agonist	5739	PTGIR	Activator
+Phenylbutazone	inhibitor	5740	PTGIS	Inhibitor
+Epoprostenol	inducer	5740	PTGIS	Activator
+Menthyl salicylate	antagonist	5742	PTGS1	Inhibitor
+Oxaprozin	inhibitor	5742	PTGS1	Inhibitor
+Sulfasalazine	inhibitor	5742	PTGS1	Inhibitor
+Trolamine salicylate	inhibitor	5742	PTGS1	Inhibitor
+Salsalate	inhibitor	5742	PTGS1	Inhibitor
+Triflusal	antagonist	5742	PTGS1	Inhibitor
+Tiaprofenic acid	inhibitor	5742	PTGS1	Inhibitor
+Phenylbutazone	inhibitor	5742	PTGS1	Inhibitor
+Meclofenamic acid	inhibitor	5742	PTGS1	Inhibitor
+Glycol salicylate	antagonist	5742	PTGS1	Inhibitor
+Salicylic acid	inhibitor	5742	PTGS1	Inhibitor
+Diclofenac	inhibitor	5742	PTGS1	Inhibitor
+Lumiracoxib	inhibitor	5742	PTGS1	Inhibitor
+Ibuprofen	inhibitor	5742	PTGS1	Inhibitor
+Balsalazide	inhibitor	5742	PTGS1	Inhibitor
+Acetaminophen	inhibitor	5742	PTGS1	Inhibitor
+Mesalazine	inhibitor	5742	PTGS1	Inhibitor
+Lornoxicam	inhibitor	5742	PTGS1	Inhibitor
+Naproxen	inhibitor	5742	PTGS1	Inhibitor
+Acemetacin	antagonist	5742	PTGS1	Inhibitor
+Loxoprofen	antagonist	5742	PTGS1	Inhibitor
+Tolfenamic acid	antagonist	5742	PTGS1	Inhibitor
+Aceclofenac	inhibitor	5742	PTGS1	Inhibitor
+Acetylsalicylic acid	inhibitor	5742	PTGS1	Inhibitor
+Icosapent	inhibitor	5742	PTGS1	Inhibitor
+Salsalate	inhibitor	5743	PTGS2	Inhibitor
+Oxaprozin	inhibitor	5743	PTGS2	Inhibitor
+Tolmetin	inhibitor	5743	PTGS2	Inhibitor
+Mesalazine	inhibitor	5743	PTGS2	Inhibitor
+Flurbiprofen	inhibitor	5743	PTGS2	Inhibitor
+Pomalidomide	inhibitor	5743	PTGS2	Inhibitor
+Menthyl salicylate	antagonist	5743	PTGS2	Inhibitor
+Glycol salicylate	antagonist	5743	PTGS2	Inhibitor
+Tenoxicam	inhibitor	5743	PTGS2	Inhibitor
+Tiaprofenic acid	inhibitor	5743	PTGS2	Inhibitor
+Sulfasalazine	inhibitor	5743	PTGS2	Inhibitor
+Ibuprofen	inhibitor	5743	PTGS2	Inhibitor
+Mefenamic acid	inhibitor	5743	PTGS2	Inhibitor
+Salicylic acid	inhibitor	5743	PTGS2	Inhibitor
+Fish oil	inhibitor	5743	PTGS2	Inhibitor
+Ketoprofen	inhibitor	5743	PTGS2	Inhibitor
+Naproxen	inhibitor	5743	PTGS2	Inhibitor
+Phenylbutazone	inhibitor	5743	PTGS2	Inhibitor
+Sulindac	inhibitor	5743	PTGS2	Inhibitor
+Parecoxib	inhibitor	5743	PTGS2	Inhibitor
+Balsalazide	inhibitor	5743	PTGS2	Inhibitor
+Diflunisal	inhibitor	5743	PTGS2	Inhibitor
+Acetylsalicylic acid	inhibitor	5743	PTGS2	Inhibitor
+Ketorolac	inhibitor	5743	PTGS2	Inhibitor
+Acemetacin	antagonist	5743	PTGS2	Inhibitor
+Lumiracoxib	inhibitor	5743	PTGS2	Inhibitor
+Fenoprofen	inhibitor	5743	PTGS2	Inhibitor
+Icosapent	inhibitor	5743	PTGS2	Inhibitor
+Meclofenamic acid	inhibitor	5743	PTGS2	Inhibitor
+Dexibuprofen	inhibitor	5743	PTGS2	Inhibitor
+Meloxicam	inhibitor	5743	PTGS2	Inhibitor
+Indomethacin	inhibitor	5743	PTGS2	Inhibitor
+Etoricoxib	inhibitor	5743	PTGS2	Inhibitor
+Aceclofenac	inhibitor	5743	PTGS2	Inhibitor
+Nabumetone	inhibitor	5743	PTGS2	Inhibitor
+Tolfenamic acid	antagonist	5743	PTGS2	Inhibitor
+Lornoxicam	inhibitor	5743	PTGS2	Inhibitor
+Loxoprofen	antagonist	5743	PTGS2	Inhibitor
+Celecoxib	inhibitor	5743	PTGS2	Inhibitor
+Diclofenac	inhibitor	5743	PTGS2	Inhibitor
+Trolamine salicylate	inhibitor	5743	PTGS2	Inhibitor
+Etodolac	inhibitor	5743	PTGS2	Inhibitor
+Acetaminophen	inhibitor	5743	PTGS2	Inhibitor
+Piroxicam	inhibitor	5743	PTGS2	Inhibitor
+Abaloparatide	ligand	5745	PTH1R	Ligand
+Parathyroid hormone	activator	5746	PTH2R	Activator
+Regorafenib	inhibitor	5894	RAF1	Inhibitor
+Sorafenib	inhibitor	5894	RAF1	Inhibitor
+Dabrafenib	inhibitor	5894	RAF1	Inhibitor
+Acitretin	agonist	5914	RARA	Activator
+Alitretinoin	agonist	5914	RARA	Activator
+Tazarotene	agonist	5914	RARA	Activator
+Acitretin	agonist	5915	RARB	Activator
+Adapalene	agonist	5915	RARB	Activator
+Alitretinoin	agonist	5915	RARB	Activator
+Tazarotene	agonist	5915	RARB	Activator
+Tazarotene	agonist	5916	RARG	Activator
+Acitretin	agonist	5916	RARG	Activator
+Palovarotene	agonist	5916	RARG	Activator
+Adapalene	agonist	5916	RARG	Activator
+Alitretinoin	agonist	5916	RARG	Activator
+Trifarotene	agonist	5916	RARG	Activator
+Tretinoin	agonist	5916	RARG	Activator
+Aliskiren	inhibitor	5972	REN	Inhibitor
+Lenvatinib	inhibitor	5979	RET	Inhibitor
+Pralsetinib	inhibitor	5979	RET	Inhibitor
+Cabozantinib	antagonist	5979	RET	Inhibitor
+Sorafenib	inhibitor	5979	RET	Inhibitor
+Selpercatinib	inhibitor	5979	RET	Inhibitor
+Regorafenib	inhibitor	5979	RET	Inhibitor
+Belumosudil	inhibitor	6093	ROCK1	Inhibitor
+Netarsudil	inhibitor	6093	ROCK1	Inhibitor
+Entrectinib	inhibitor	6098	ROS1	Inhibitor
+Crizotinib	inhibitor	6098	ROS1	Inhibitor
+Voretigene neparvovec	gene replacement	6121	RPE65	other
+Omacetaxine mepesuccinate	antagonist	6122	RPL3	Inhibitor
+Fludarabine	inhibitor	6240	RRM1	Inhibitor
+Hydroxyurea	inhibitor	6240	RRM1	Inhibitor
+Gemcitabine	inhibitor	6240	RRM1	Inhibitor
+Clofarabine	inhibitor	6240	RRM1	Inhibitor
+Cladribine	inhibitor	6240	RRM1	Inhibitor
+Gallium nitrate	inhibitor	6241	RRM2	Inhibitor
+Cladribine	inhibitor	6241	RRM2	Inhibitor
+Adapalene	agonist	6256	RXRA	Activator
+Bexarotene	agonist	6256	RXRA	Activator
+Alitretinoin	agonist	6256	RXRA	Activator
+Acitretin	agonist	6256	RXRA	Activator
+Doconexent	activator	6256	RXRA	Activator
+Adapalene	agonist	6257	RXRB	Activator
+Tretinoin	agonist	6257	RXRB	Activator
+Doconexent	activator	6257	RXRB	Activator
+Acitretin	agonist	6257	RXRB	Activator
+Tazarotene	agonist	6257	RXRB	Activator
+Bexarotene	agonist	6257	RXRB	Activator
+Alitretinoin	agonist	6257	RXRB	Activator
+Doconexent	activator	6258	RXRG	Activator
+Adapalene	agonist	6258	RXRG	Activator
+Acitretin	agonist	6258	RXRG	Activator
+Alitretinoin	agonist	6258	RXRG	Activator
+Bexarotene	agonist	6258	RXRG	Activator
+Tretinoin	agonist	6258	RXRG	Activator
+Tetracaine	modulator	6261	RYR1	Inhibitor
+Dantrolene	antagonist	6261	RYR1	Inhibitor
+Tetracaine	modulator	6262	RYR2	Inhibitor
+Permethrin	inhibitor	6323	SCN1A	Inhibitor
+Phenazopyridine	inhibitor	6323	SCN1A	Inhibitor
+Phenacemide	inhibitor	6323	SCN1A	Inhibitor
+Phenytoin	inhibitor	6323	SCN1A	Inhibitor
+Zonisamide	inhibitor	6323	SCN1A	Inhibitor
+Zonisamide	inhibitor	6324	SCN1B	Inhibitor
+Phenytoin	inhibitor	6326	SCN2A	Inhibitor
+Tramadol	inhibitor	6326	SCN2A	Inhibitor
+Zonisamide	inhibitor	6326	SCN2A	Inhibitor
+Zonisamide	inhibitor	6327	SCN2B	Inhibitor
+Zonisamide	inhibitor	6328	SCN3A	Inhibitor
+Zonisamide	inhibitor	6329	SCN4A	Inhibitor
+Zonisamide	inhibitor	6330	SCN4B	Inhibitor
+Quinidine	inhibitor	6331	SCN5A	Inhibitor
+Quinidine barbiturate	inhibitor	6331	SCN5A	Inhibitor
+Phenytoin	inhibitor	6331	SCN5A	Inhibitor
+Propafenone	inhibitor	6331	SCN5A	Inhibitor
+Ajmaline	inhibitor	6331	SCN5A	Inhibitor
+Dronedarone	inhibitor	6331	SCN5A	Inhibitor
+Riluzole	inhibitor	6331	SCN5A	Inhibitor
+Vernakalant	blocker	6331	SCN5A	Inhibitor
+Disopyramide	inhibitor	6331	SCN5A	Inhibitor
+Tocainide	inhibitor	6331	SCN5A	Inhibitor
+Zonisamide	inhibitor	6331	SCN5A	Inhibitor
+Cinchocaine	inhibitor	6331	SCN5A	Inhibitor
+Mexiletine	inhibitor	6331	SCN5A	Inhibitor
+Ethotoin	inhibitor	6331	SCN5A	Inhibitor
+Prilocaine	inhibitor	6331	SCN5A	Inhibitor
+Fosphenytoin	inhibitor	6331	SCN5A	Inhibitor
+Lidocaine	inhibitor	6331	SCN5A	Inhibitor
+Indecainide	inhibitor	6331	SCN5A	Inhibitor
+Phenytoin	inhibitor	6334	SCN8A	Inhibitor
+Lidocaine	inhibitor	6335	SCN9A	Inhibitor
+Zonisamide	inhibitor	6335	SCN9A	Inhibitor
+Mepivacaine	inhibitor	6336	SCN10A	Inhibitor
+Dyclonine	inhibitor	6336	SCN10A	Inhibitor
+Benzocaine	inhibitor	6336	SCN10A	Inhibitor
+Cinchocaine	inhibitor	6336	SCN10A	Inhibitor
+Proparacaine	inhibitor	6336	SCN10A	Inhibitor
+Procaine	inhibitor	6336	SCN10A	Inhibitor
+Lidocaine	inhibitor	6336	SCN10A	Inhibitor
+Bupivacaine	inhibitor	6336	SCN10A	Inhibitor
+Ropivacaine	inhibitor	6336	SCN10A	Inhibitor
+Oxybuprocaine	inhibitor	6336	SCN10A	Inhibitor
+Levobupivacaine	inhibitor	6336	SCN10A	Inhibitor
+Triamterene	inhibitor	6337	SCNN1A	Inhibitor
+Amiloride	inhibitor	6337	SCNN1A	Inhibitor
+Amiloride	inhibitor	6338	SCNN1B	Inhibitor
+Triamterene	inhibitor	6338	SCNN1B	Inhibitor
+Amiloride	inhibitor	6339	SCNN1D	Inhibitor
+Triamterene	inhibitor	6340	SCNN1G	Inhibitor
+Amiloride	inhibitor	6340	SCNN1G	Inhibitor
+Secretin human	ligand	6344	SCTR	Ligand
+Ubidecarenone	cofactor	6389	SDHA	other
+Crizanlizumab	inhibitor	6403	SELP	Inhibitor
+Tamoxifen	inducer	6462	SHBG	Activator
+Acarbose	inhibitor	6476	SI	Inhibitor
+Tebentafusp	binder	6490	PMEL	Ligand
+Fluciclovine (18F)	binder	6510	SLC1A5	Ligand
+Sotagliflozin	inhibitor	6523	SLC5A1	Inhibitor
+Ertugliflozin	antagonist	6524	SLC5A2	Inhibitor
+Sotagliflozin	inhibitor	6524	SLC5A2	Inhibitor
+Empagliflozin	inhibitor	6524	SLC5A2	Inhibitor
+Dapagliflozin	antagonist|inhibitor	6524	SLC5A2	Activator
+Canagliflozin	inhibitor	6524	SLC5A2	Inhibitor
+Potassium perchlorate	inhibitor	6528	SLC5A5	Inhibitor
+Iodine	substrate	6528	SLC5A5	Activator
+Tiagabine	inhibitor	6529	SLC6A1	Inhibitor
+Phentermine	inhibitor	6530	SLC6A2	Inhibitor
+Desipramine	inhibitor	6530	SLC6A2	Inhibitor
+Duloxetine	inhibitor	6530	SLC6A2	Inhibitor
+Dosulepin	inhibitor	6530	SLC6A2	Inhibitor
+Netarsudil	inhibitor	6530	SLC6A2	Inhibitor
+Dexmethylphenidate	inhibitor	6530	SLC6A2	Inhibitor
+Nortriptyline	inhibitor	6530	SLC6A2	Inhibitor
+Debrisoquine	inducer	6530	SLC6A2	Activator
+Bupropion	inhibitor	6530	SLC6A2	Inhibitor
+Pseudoephedrine	inhibitor	6530	SLC6A2	Inhibitor
+Serdexmethylphenidate	inhibitor	6530	SLC6A2	Inhibitor
+Protriptyline	inhibitor	6530	SLC6A2	Inhibitor
+Venlafaxine	inhibitor	6530	SLC6A2	Inhibitor
+Tapentadol	inhibitor	6530	SLC6A2	Inhibitor
+Trimipramine	inhibitor	6530	SLC6A2	Inhibitor
+Maprotiline	inhibitor	6530	SLC6A2	Inhibitor
+Levomilnacipran	inhibitor	6530	SLC6A2	Inhibitor
+Phendimetrazine	negative modulator	6530	SLC6A2	Inhibitor
+Imipramine	inhibitor	6530	SLC6A2	Inhibitor
+Dextroamphetamine	negative modulator	6530	SLC6A2	Inhibitor
+Amitriptyline	inhibitor	6530	SLC6A2	Inhibitor
+Desvenlafaxine	inhibitor	6530	SLC6A2	Inhibitor
+Orphenadrine	inhibitor	6530	SLC6A2	Inhibitor
+Atomoxetine	inhibitor	6530	SLC6A2	Inhibitor
+Diethylpropion	inhibitor	6530	SLC6A2	Inhibitor
+Guanadrel	partial agonist	6530	SLC6A2	Modulator
+Milnacipran	inhibitor	6530	SLC6A2	Inhibitor
+Reboxetine	inhibitor	6530	SLC6A2	Inhibitor
+Cocaine	inhibitor	6530	SLC6A2	Inhibitor
+Mazindol	inhibitor	6530	SLC6A2	Inhibitor
+Phenmetrazine	inhibitor	6530	SLC6A2	Inhibitor
+Doxepin	inhibitor	6530	SLC6A2	Inhibitor
+Guanethidine	inducer	6530	SLC6A2	Activator
+Tramadol	inhibitor	6530	SLC6A2	Inhibitor
+Dextroamphetamine	negative modulator	6531	SLC6A3	Inhibitor
+Bupropion	inhibitor	6531	SLC6A3	Inhibitor
+Serdexmethylphenidate	inhibitor	6531	SLC6A3	Inhibitor
+Cocaine	inhibitor	6531	SLC6A3	Inhibitor
+Procaine	inhibitor	6531	SLC6A3	Inhibitor
+Pseudoephedrine	inhibitor	6531	SLC6A3	Inhibitor
+Amphetamine	negative modulator	6531	SLC6A3	Inhibitor
+Armodafinil	antagonist|inhibitor	6531	SLC6A3	Activator
+Dexmethylphenidate	inhibitor	6531	SLC6A3	Inhibitor
+Phenmetrazine	inhibitor	6531	SLC6A3	Inhibitor
+Dopamine	inducer	6531	SLC6A3	Activator
+Phentermine	inhibitor	6531	SLC6A3	Inhibitor
+Methylphenidate	inhibitor	6531	SLC6A3	Inhibitor
+Mazindol	inhibitor	6531	SLC6A3	Inhibitor
+Diethylpropion	inhibitor	6531	SLC6A3	Inhibitor
+Benzatropine	inhibitor	6531	SLC6A3	Inhibitor
+Modafinil	inhibitor	6531	SLC6A3	Inhibitor
+Vilazodone	inhibitor	6532	SLC6A4	Inhibitor
+Levomilnacipran	inhibitor	6532	SLC6A4	Inhibitor
+Fluvoxamine	inhibitor	6532	SLC6A4	Inhibitor
+Amoxapine	inhibitor	6532	SLC6A4	Inhibitor
+Amitriptyline	inhibitor	6532	SLC6A4	Inhibitor
+Escitalopram	inhibitor	6532	SLC6A4	Inhibitor
+Venlafaxine	inhibitor	6532	SLC6A4	Inhibitor
+Vortioxetine	inhibitor	6532	SLC6A4	Inhibitor
+Dosulepin	inhibitor	6532	SLC6A4	Inhibitor
+Desipramine	inhibitor	6532	SLC6A4	Inhibitor
+Tramadol	inhibitor	6532	SLC6A4	Inhibitor
+Mazindol	inhibitor	6532	SLC6A4	Inhibitor
+Imipramine	inhibitor	6532	SLC6A4	Inhibitor
+Desvenlafaxine	inhibitor	6532	SLC6A4	Inhibitor
+Clomipramine	inhibitor	6532	SLC6A4	Inhibitor
+Milnacipran	inhibitor	6532	SLC6A4	Inhibitor
+Protriptyline	inhibitor	6532	SLC6A4	Inhibitor
+Cocaine	inhibitor	6532	SLC6A4	Inhibitor
+Sertraline	inhibitor|binder|downregulator	6532	SLC6A4	Inhibitor
+Lorpiprazole	antagonist	6532	SLC6A4	Inhibitor
+Citalopram	inhibitor	6532	SLC6A4	Inhibitor
+Trimipramine	inhibitor	6532	SLC6A4	Inhibitor
+Pseudoephedrine	inhibitor	6532	SLC6A4	Inhibitor
+Phentermine	inhibitor	6532	SLC6A4	Inhibitor
+Doxepin	inhibitor	6532	SLC6A4	Inhibitor
+Lumateperone	inhibitor	6532	SLC6A4	Inhibitor
+Paroxetine	inhibitor	6532	SLC6A4	Inhibitor
+Duloxetine	inhibitor	6532	SLC6A4	Inhibitor
+Trazodone	inhibitor	6532	SLC6A4	Inhibitor
+Fluoxetine	inhibitor	6532	SLC6A4	Inhibitor
+Nortriptyline	inhibitor	6532	SLC6A4	Inhibitor
+Creatine	unknown	6535	SLC6A8	other
+Icosapent	inhibitor	6546	SLC8A1	Inhibitor
+Dronedarone	inhibitor	6546	SLC8A1	Inhibitor
+Tenapanor	inhibitor	6550	SLC9A3	Inhibitor
+Bulevirtide	inhibitor	6554	SLC10A1	Inhibitor
+Maralixibat	inhibitor	6555	SLC10A2	Inhibitor
+Odevixibat	inhibitor	6555	SLC10A2	Inhibitor
+Torasemide	inhibitor	6557	SLC12A1	Inhibitor
+Furosemide	inhibitor	6557	SLC12A1	Inhibitor
+Bumetanide	inhibitor	6557	SLC12A1	Inhibitor
+Chlorthalidone	inhibitor	6557	SLC12A1	Inhibitor
+Quinethazone	inhibitor	6557	SLC12A1	Inhibitor
+Etacrynic acid	inhibitor	6557	SLC12A1	Inhibitor
+Torasemide	inhibitor	6558	SLC12A2	Inhibitor
+Bumetanide	inhibitor	6558	SLC12A2	Inhibitor
+Quinethazone	inhibitor	6558	SLC12A2	Inhibitor
+Benzthiazide	inhibitor	6559	SLC12A3	Inhibitor
+Hydrochlorothiazide	inhibitor	6559	SLC12A3	Inhibitor
+Quinethazone	inhibitor	6559	SLC12A3	Inhibitor
+Metolazone	inhibitor	6559	SLC12A3	Inhibitor
+Bendroflumethiazide	inhibitor	6559	SLC12A3	Inhibitor
+Indapamide	inhibitor	6559	SLC12A3	Inhibitor
+Polythiazide	inhibitor	6559	SLC12A3	Inhibitor
+Trichlormethiazide	inhibitor	6559	SLC12A3	Inhibitor
+Chlorothiazide	inhibitor	6559	SLC12A3	Inhibitor
+Bumetanide	inhibitor	6560	SLC12A4	Inhibitor
+Isometheptene	inhibitor	6571	SLC18A2	Inhibitor
+Amphetamine	inhibitor	6571	SLC18A2	Inhibitor
+Deutetrabenazine	inhibitor	6571	SLC18A2	Inhibitor
+Propylhexedrine	unknown	6571	SLC18A2	other
+Benzphetamine	inducer	6571	SLC18A2	Activator
+Dextroamphetamine	inducer	6571	SLC18A2	Activator
+Deserpidine	inhibitor	6571	SLC18A2	Inhibitor
+Tetrabenazine	inhibitor	6571	SLC18A2	Inhibitor
+Nusinersen	antisense oligonucleotide	6606	SMN1	Activator
+Vismodegib	antagonist	6608	SMO	Inhibitor
+Glasdegib	inhibitor	6608	SMO	Inhibitor
+Letibotulinumtoxina	cleavage	6616	SNAP25	Inhibitor
+Botulinum toxin type A	inhibitor	6616	SNAP25	Inhibitor
+Ezetimibe	inhibitor	6646	SOAT1	Inhibitor
+Butenafine	inhibitor	6713	SQLE	Inhibitor
+Naftifine	inhibitor	6713	SQLE	Inhibitor
+Dasatinib	multitarget	6714	SRC	Inhibitor
+Tirbanibulin	inhibitor	6714	SRC	Inhibitor
+Norgestrel	inhibitor	6715	SRD5A1	Inhibitor
+Dutasteride	inhibitor	6715	SRD5A1	Inhibitor
+Levonorgestrel	inhibitor	6715	SRD5A1	Inhibitor
+Finasteride	inhibitor	6716	SRD5A2	Inhibitor
+Dutasteride	inhibitor	6716	SRD5A2	Inhibitor
+Azelaic acid	inhibitor	6716	SRD5A2	Inhibitor
+Azelaic acid	inhibitor	6718	AKR1D1	Inhibitor
+Lutetium Lu 177 dotatate	agonist	6751	SSTR1	Activator
+Edotreotide gallium Ga-68	ligand	6751	SSTR1	Ligand
+Somatostatin	agonist	6751	SSTR1	Activator
+Lutetium Lu 177 dotatate	agonist	6752	SSTR2	Activator
+Edotreotide gallium Ga-68	ligand	6752	SSTR2	Ligand
+Copper oxodotreotide Cu-64	binder	6752	SSTR2	Ligand
+Somatostatin	agonist	6752	SSTR2	Activator
+Dotatate gallium Ga-68	binder	6752	SSTR2	Ligand
+Somatostatin	agonist	6753	SSTR3	Activator
+Edotreotide gallium Ga-68	ligand	6753	SSTR3	Ligand
+Lutetium Lu 177 dotatate	agonist	6753	SSTR3	Activator
+Lutetium Lu 177 dotatate	agonist	6754	SSTR4	Activator
+Somatostatin	agonist	6754	SSTR4	Activator
+Somatostatin	agonist	6755	SSTR5	Activator
+Edotreotide gallium Ga-68	ligand	6755	SSTR5	Ligand
+Lutetium Lu 177 dotatate	agonist	6755	SSTR5	Activator
+Omega-3-carboxylic acids	potentiator	6785	ELOVL4	Activator
+Glipizide	inhibitor	6833	ABCC8	Inhibitor
+Glimepiride	inducer	6833	ABCC8	Activator
+Gliquidone	inhibitor	6833	ABCC8	Inhibitor
+Gliclazide	binder	6833	ABCC8	Ligand
+Chlorpropamide	inhibitor	6833	ABCC8	Inhibitor
+Repaglinide	inhibitor	6833	ABCC8	Inhibitor
+Tolbutamide	inhibitor	6833	ABCC8	Inhibitor
+Nateglinide	inhibitor	6833	ABCC8	Inhibitor
+Botulinum toxin type B	binder	6843	VAMP1	Ligand
+Botulinum toxin type B	binder	6844	VAMP2	Ligand
+Fostamatinib	inhibitor	6850	SYK	Inhibitor
+Fosnetupitant	antagonist	6869	TACR1	Inhibitor
+Aprepitant	antagonist	6869	TACR1	Inhibitor
+Rolapitant	antagonist	6869	TACR1	Inhibitor
+Netupitant	antagonist	6869	TACR1	Inhibitor
+Ripretinib	inhibitor	7010	TEK	Inhibitor
+Regorafenib	inhibitor	7010	TEK	Inhibitor
+Dalteparin	inhibitor	7035	TFPI	Inhibitor
+Andexanet alfa	inhibitor	7035	TFPI	Inhibitor
+Ferric cation	agonist	7037	TFRC	Activator
+Tetraferric tricitrate decahydrate	ligand	7037	TFRC	Ligand
+Terazosin	inducer	7040	TGFB1	Activator
+Foreskin keratinocyte (neonatal)	agonist	7040	TGFB1	Activator
+Foreskin keratinocyte (neonatal)	agonist	7048	TGFBR2	Activator
+Metyrosine	binder	7054	TH	Ligand
+Sapropterin	cofactor	7054	TH	other
+Liotrix	agonist	7067	THRA	Activator
+Liothyronine	agonist	7067	THRA	Activator
+Dextrothyroxine	agonist	7067	THRA	Activator
+Levothyroxine	agonist	7067	THRA	Activator
+Levothyroxine	agonist	7068	THRB	Activator
+Liotrix	agonist	7068	THRB	Activator
+Dextrothyroxine	agonist	7068	THRB	Activator
+Liothyronine	agonist	7068	THRB	Activator
+Brivudine	substrate	7084	TK2	Activator
+Adapalene	antagonist	7097	TLR2	Inhibitor
+Tuberculin purified protein derivative	ligand	7097	TLR2	Ligand
+Mifamurtide	ligand	7099	TLR4	Ligand
+Pomalidomide	inhibitor	7124	TNF	Inhibitor
+Glycyrrhizic acid	antagonist	7124	TNF	Inhibitor
+Golimumab	antibody	7124	TNF	Modulator
+Foreskin keratinocyte (neonatal)	agonist	7124	TNF	Activator
+Adalimumab	inhibitor|antibody	7124	TNF	Inhibitor
+Infliximab	inhibitor	7124	TNF	Inhibitor
+Etanercept	inhibitor|antibody	7124	TNF	Inhibitor
+Certolizumab pegol	neutralizer	7124	TNF	other
+Tasonermin	agonist	7132	TNFRSF1A	Activator
+Levosimendan	potentiator	7134	TNNC1	Activator
+Sacituzumab govitecan	inhibitor	7150	TOP1	Inhibitor
+Trastuzumab deruxtecan	inhibitor	7150	TOP1	Inhibitor
+Topotecan	inhibitor	7150	TOP1	Inhibitor
+Doxorubicin	inhibitor	7150	TOP1	Inhibitor
+Sodium stibogluconate	inhibitor	7150	TOP1	Inhibitor
+Irinotecan	inhibitor	7150	TOP1	Inhibitor
+Amsacrine	inhibitor	7153	TOP2A	Inhibitor
+Idarubicin	inhibitor	7153	TOP2A	Inhibitor
+Finafloxacin	inhibitor	7153	TOP2A	Inhibitor
+Mitoxantrone	inhibitor	7153	TOP2A	Inhibitor
+Teniposide	inhibitor	7153	TOP2A	Inhibitor
+Etoposide	inhibitor	7153	TOP2A	Inhibitor
+Doxorubicin	inhibitor	7153	TOP2A	Inhibitor
+Podofilox	inhibitor	7153	TOP2A	Inhibitor
+Daunorubicin	inhibitor	7153	TOP2A	Inhibitor
+Valrubicin	inhibitor	7153	TOP2A	Inhibitor
+Doxorubicin	inhibitor	7155	TOP2B	Inhibitor
+Daunorubicin	inhibitor	7155	TOP2B	Inhibitor
+Telotristat ethyl	antagonist	7166	TPH1	Inhibitor
+Sapropterin	cofactor	7166	TPH1	other
+Olsalazine	inhibitor	7172	TPMT	Inhibitor
+Carbimazole	inhibitor	7173	TPO	Inhibitor
+Propylthiouracil	inhibitor	7173	TPO	Inhibitor
+Methimazole	substrate|inhibitor	7173	TPO	Activator
+Protirelin	ligand	7201	TRHR	Ligand
+Thyrotropin alfa	agonist	7253	TSHR	Activator
+Tafamidis	chaperone	7276	TTR	other
+Cabazitaxel	binder	7277	TUBA4A	Ligand
+Podofilox	inhibitor	7277	TUBA4A	Inhibitor
+Vinblastine	binder	7283	TUBG1	Ligand
+Arsenic trioxide	inhibitor	7296	TXNRD1	Inhibitor
+Ruxolitinib	inhibitor	7297	TYK2	Inhibitor
+Deucravacitinib	inhibitor	7297	TYK2	Inhibitor
+Tegafur-uracil	antagonist	7298	TYMS	Inhibitor
+Capecitabine	inhibitor	7298	TYMS	Inhibitor
+Methotrexate	inhibitor	7298	TYMS	Inhibitor
+Tegafur	inhibitor	7298	TYMS	Inhibitor
+Trifluridine	inhibitor	7298	TYMS	Inhibitor
+Pemetrexed	inhibitor	7298	TYMS	Inhibitor
+Fluorouracil	unknown	7298	TYMS	other
+Floxuridine	unknown	7298	TYMS	other
+Raltitrexed	inhibitor	7298	TYMS	Inhibitor
+Hydroquinone	inhibitor	7299	TYR	Inhibitor
+Azelaic acid	inhibitor	7299	TYR	Inhibitor
+Arbutin	inhibitor	7299	TYR	Inhibitor
+Miglustat	inhibitor	7357	UGCG	Inhibitor
+Eliglustat	inhibitor	7357	UGCG	Inhibitor
+Alfacalcidol	agonist	7421	VDR	Activator
+Ergocalciferol	agonist	7421	VDR	Activator
+Calcitriol	antagonist	7421	VDR	Inhibitor
+Cholecalciferol	agonist	7421	VDR	Activator
+Dihydrotachysterol	agonist	7421	VDR	Activator
+Paricalcitol	agonist	7421	VDR	Activator
+Calcifediol	agonist	7421	VDR	Activator
+Pegaptanib	antagonist	7422	VEGFA	Inhibitor
+Ranibizumab	antibody	7422	VEGFA	Modulator
+Aflibercept	binder	7422	VEGFA	Ligand
+Brolucizumab	inhibitor	7422	VEGFA	Inhibitor
+Faricimab	antagonist	7422	VEGFA	Inhibitor
+Bevacizumab	unknown	7422	VEGFA	other
+Foreskin keratinocyte (neonatal)	agonist	7422	VEGFA	Activator
+Dalteparin	inhibitor	7422	VEGFA	Inhibitor
+Aflibercept	binder	7423	VEGFB	Ligand
+Capsaicin	agonist|regulator	7442	TRPV1	Activator
+alpha-Linolenic acid	inhibitor	7442	TRPV1	Inhibitor
+Capsicum oleoresin	agonist|activator	7442	TRPV1	Activator
+Zucapsaicin	agonist|activator	7442	TRPV1	Activator
+"Antihemophilic factor, human recombinant"	binder	7450	VWF	Ligand
+Efmoroctocog alfa	binding	7450	VWF	Ligand
+Moroctocog alfa	binder	7450	VWF	Ligand
+Simoctocog alfa	binding	7450	VWF	Ligand
+Rurioctocog alfa pegol	binder	7450	VWF	Ligand
+Turoctocog alfa pegol	binder	7450	VWF	Ligand
+"Antihemophilic factor (recombinant), PEGylated"	binder	7450	VWF	Ligand
+Lonoctocog alfa	binder	7450	VWF	Ligand
+Susoctocog alfa	binding	7450	VWF	Ligand
+Allopurinol	inhibitor	7498	XDH	Inhibitor
+Febuxostat	inhibitor	7498	XDH	Inhibitor
+Selinexor	inhibitor	7514	XPO1	Inhibitor
+Dasatinib	inhibitor	7525	YES1	Inhibitor
+Vinblastine	binder	7846	TUBA1A	Ligand
+Mebendazole	inhibitor	7846	TUBA1A	Inhibitor
+Plerixafor	antagonist|inhibitor	7852	CXCR4	Activator
+Taurine	agonist	8001	GLRA3	Activator
+Agkistrodon piscivorus antivenin	antagonist	8399	PLA2G10	Inhibitor
+Crotalus adamanteus antivenin	antagonist	8399	PLA2G10	Inhibitor
+Orlistat	inhibitor	8513	LIPF	Inhibitor
+Pyridoxine	ligand	8566	PDXK	Ligand
+Lenalidomide	inhibitor	8600	TNFSF11	Inhibitor
+Denosumab	antibody	8600	TNFSF11	Modulator
+Hydralazine	inhibitor	8639	AOC3	Inhibitor
+Theophylline	inhibitor	8654	PDE5A	Inhibitor
+Tadalafil	inhibitor	8654	PDE5A	Inhibitor
+Avanafil	inhibitor	8654	PDE5A	Inhibitor
+Vardenafil	inhibitor	8654	PDE5A	Inhibitor
+Sildenafil	inhibitor	8654	PDE5A	Inhibitor
+Dipyridamole	inhibitor	8654	PDE5A	Inhibitor
+Udenafil	inhibitor	8654	PDE5A	Inhibitor
+Spesolimab	antagonist	8808	IL1RL2	Inhibitor
+Vorinostat	inhibitor	8841	HDAC3	Inhibitor
+Niacin	agonist	8843	HCAR3	Activator
+Erlotinib	agonist	8856	NR1I2	Activator
+Amlodipine	inhibitor	8911	CACNA1I	Inhibitor
+Paramethadione	inhibitor	8911	CACNA1I	Inhibitor
+Cinnarizine	inhibitor	8911	CACNA1I	Inhibitor
+Zonisamide	inhibitor	8911	CACNA1I	Inhibitor
+Flunarizine	inhibitor	8911	CACNA1I	Inhibitor
+Isradipine	inhibitor	8912	CACNA1H	Inhibitor
+Zonisamide	inhibitor	8912	CACNA1H	Inhibitor
+Cinnarizine	inhibitor	8912	CACNA1H	Inhibitor
+Flunarizine	inhibitor	8912	CACNA1H	Inhibitor
+Methsuximide	inhibitor	8913	CACNA1G	Inhibitor
+Ethosuximide	inhibitor	8913	CACNA1G	Inhibitor
+Flunarizine	inhibitor	8913	CACNA1G	Inhibitor
+Cinnarizine	inhibitor	8913	CACNA1G	Inhibitor
+Trimethadione	inhibitor	8913	CACNA1G	Inhibitor
+Zonisamide	inhibitor	8913	CACNA1G	Inhibitor
+Acarbose	inhibitor	8972	MGAM	Inhibitor
+Miglitol	antagonist|inhibitor	8972	MGAM	Activator
+Levomenthol	inducer	8989	TRPA1	Activator
+Fluciclovine (18F)	binder	9056	SLC7A7	Ligand
+Isradipine	inhibitor	9254	CACNA2D2	Inhibitor
+Dalfampridine	antagonist	9312	KCNB2	Inhibitor
+Teduglutide	agonist	9340	GLP2R	Activator
+Probenecid	inhibitor	9356	SLC22A6	Inhibitor
+Probenecid	inhibitor	9376	SLC22A8	Inhibitor
+alpha-Linolenic acid	ligand	9415	FADS2	Ligand
+Zoledronic acid	inhibitor	9453	GGPS1	Inhibitor
+Ibandronate	inhibitor	9453	GGPS1	Inhibitor
+Netarsudil	inhibitor	9475	ROCK2	Inhibitor
+Belumosudil	inhibitor	9475	ROCK2	Inhibitor
+Baclofen	agonist	9568	GABBR2	Activator
+Amphetamine	agonist	9607	CARTPT	Activator
+Valproic acid	inhibitor	9734	HDAC9	Inhibitor
+Dimethyl fumarate	binder	9817	KEAP1	Ligand
+Brivaracetam	unknown	9900	SV2A	other
+Levetiracetam	agonist	9900	SV2A	Activator
+Obeticholic acid	agonist	9971	NR1H4	Activator
+Vorinostat	inhibitor	10013	HDAC6	Inhibitor
+Talazoparib	inhibitor	10038	PARP2	Inhibitor
+Rucaparib	inhibitor	10038	PARP2	Inhibitor
+Olaparib	inhibitor	10038	PARP2	Inhibitor
+Niraparib	inhibitor	10038	PARP2	Inhibitor
+Olaparib	inhibitor	10039	PARP3	Inhibitor
+Rucaparib	inhibitor	10039	PARP3	Inhibitor
+Nicorandil	activator	10060	ABCC9	Activator
+Cyclosporine	binder	10105	PPIF	Ligand
+Rimegepant	antagonist	10203	CALCRL	Inhibitor
+Ubrogepant	antagonist	10203	CALCRL	Inhibitor
+Atogepant	antagonist	10203	CALCRL	Inhibitor
+Galsulfase	unknown	10226	PLIN3	other
+Pramlintide	agonist	10266	RAMP2	Activator
+Pramlintide	agonist	10267	RAMP1	Activator
+Pramlintide	agonist	10268	RAMP3	Activator
+Dextromethorphan	agonist	10280	SIGMAR1	Activator
+Pentazocine	agonist	10280	SIGMAR1	Activator
+Ixabepilone	inhibitor	10381	TUBB3	Inhibitor
+Mebendazole	inhibitor	10383	TUBB4B	Inhibitor
+Tetradecyl hydrogen sulfate (ester)	antagonist	10544	PROCR	Inhibitor
+Belimumab	antibody	10673	TNFSF13B	Modulator
+Zafirlukast	antagonist	10800	CYSLTR1	Inhibitor
+Montelukast	antagonist	10800	CYSLTR1	Inhibitor
+Nedocromil	antagonist	10800	CYSLTR1	Inhibitor
+Triflusal	antagonist	10846	PDE10A	Inhibitor
+Dipyridamole	inhibitor	10846	PDE10A	Inhibitor
+Moxonidine	agonist	11188	NISCH	Activator
+Betahistine	antagonist	11255	HRH3	Inhibitor
+Histamine	agonist	11255	HRH3	Activator
+Pitolisant	antagonist|inverse agonist	11255	HRH3	Activator
+Zonisamide	inhibitor	11280	SCN11A	Inhibitor
+Miglitol	antagonist	23193	GANAB	Inhibitor
+Niacin	binder	23475	QPRT	Ligand
+Levocabastine	partial antagonist	23620	NTSR2	Activator
+Acetazolamide	inhibitor	23632	CA14	Inhibitor
+Acetylcysteine	activator	23657	SLC7A11	Activator
+Riluzole	inducer	23657	SLC7A11	Activator
+Auranofin	inhibitor	25824	PRDX5	Inhibitor
+Thiamine	substrate	27010	TPK1	Activator
+Isoflurane	inhibitor	27032	ATP2C1	Inhibitor
+Evinacumab	inhibitor|binder|antibody	27329	ANGPTL3	Inhibitor
+Durvalumab	inhibitor|antibody	29126	CD274	Inhibitor
+Atezolizumab	inhibitor|antibody	29126	CD274	Inhibitor
+Avelumab	inhibitor|antibody	29126	CD274	Inhibitor
+Ezetimibe	inhibitor	29881	NPC1L1	Inhibitor
+Cladribine	inhibitor	50484	RRM2B	Inhibitor
+Trifluoperazine	antagonist	50632	CALY	Inhibitor
+Romosozumab	inhibitor	50964	SOST	Inhibitor
+Vinblastine	binder	51174	TUBD1	Ligand
+Vinblastine	binder	51175	TUBE1	Ligand
+Lenalidomide	inhibitor	51185	CRBN	Inhibitor
+Pomalidomide	inhibitor	51185	CRBN	Inhibitor
+Imiquimod	agonist	51284	TLR7	Activator
+Hydroxychloroquine	antagonist	51284	TLR7	Inhibitor
+Halothane	binder	51305	KCNK9	Ligand
+Doxapram	inhibitor	51305	KCNK9	Inhibitor
+Guselkumab	blocker	51561	IL23A	Inhibitor
+Ozanimod	agonist	53637	S1PR5	Activator
+Fingolimod	modulator	53637	S1PR5	Inhibitor
+Hydroxychloroquine	antagonist	54106	TLR9	Inhibitor
+Cladribine	inhibitor	54107	POLE3	Inhibitor
+Lumasiran	antisense oligonucleotide	54363	HAO1	Activator
+Roxadustat	inhibitor	54583	EGLN1	Inhibitor
+Masoprocol	inhibitor	54822	TRPM7	Inhibitor
+Crofelemer	antagonist	55107	ANO1	Inhibitor
+Riboflavin	ligand	55312	RFK	Ligand
+Zonisamide	inhibitor	55800	SCN3B	Inhibitor
+Lesinurad	inhibitor	55867	SLC22A11	Inhibitor
+Probenecid	inhibitor	55867	SLC22A11	Inhibitor
+Cladribine	inhibitor	56655	POLE4	Inhibitor
+Trimethaphan	antagonist	57053	CHRNA10	Inhibitor
+Pentolinium	antagonist	57053	CHRNA10	Inhibitor
+Nedocromil	antagonist	57105	CYSLTR2	Inhibitor
+Bumetanide	inhibitor	57468	SLC12A5	Inhibitor
+Elotuzumab	modulator	57823	SLAMF7	Inhibitor
+Pheniramine	inverse agonist	59340	HRH4	Modulator
+Mifamurtide	ligand	64127	NOD2	Ligand
+Ticagrelor	inhibitor	64805	P2RY12	Inhibitor
+Cangrelor	inhibitor	64805	P2RY12	Inhibitor
+Ticlopidine	antagonist	64805	P2RY12	Inhibitor
+Epoprostenol	agonist	64805	P2RY12	Activator
+Clopidogrel	antagonist	64805	P2RY12	Inhibitor
+Prasugrel	antagonist	64805	P2RY12	Inhibitor
+Phenindione	inhibitor	79001	VKORC1	Inhibitor
+Dicoumarol	inhibitor	79001	VKORC1	Inhibitor
+Acenocoumarol	inhibitor	79001	VKORC1	Inhibitor
+Warfarin	inhibitor	79001	VKORC1	Inhibitor
+Menadione	cofactor	79001	VKORC1	other
+Phenprocoumon	inhibitor	79001	VKORC1	Inhibitor
+Levomenthol	inducer	79054	TRPM8	Activator
+gamma-Hydroxybutyric acid	agonist	79581	SLC52A2	Activator
+Sevoflurane	activator	79714	CCDC51	Activator
+Docetaxel	unknown	81027	TUBB1	other
+Cabazitaxel	binder	81027	TUBB1	Ligand
+Vindesine	inhibitor	81027	TUBB1	Inhibitor
+Paclitaxel	inhibitor	81027	TUBB1	Inhibitor
+Omega-3-carboxylic acids	antagonist	84649	DGAT2	Inhibitor
+Fish oil	agonist	84649	DGAT2	Activator
+Fluciclovine (18F)	binder	84889	SLC7A3	Ligand
+Tezepelumab	binder|antibody	85480	TSLP	Inhibitor
+Methadone	agonist	89832	CHRFAM7A	Activator
+Galantamine	allosteric modulator	89832	CHRFAM7A	Modulator
+Nicotine	agonist	89832	CHRFAM7A	Activator
+Roxadustat	inhibitor	112398	EGLN2	Inhibitor
+Roxadustat	inhibitor	112399	EGLN3	Inhibitor
+Bimekizumab	antibody	112744	IL17F	Modulator
+Lesinurad	inhibitor	116085	SLC22A12	Inhibitor
+Sulfinpyrazone	inhibitor	116085	SLC22A12	Inhibitor
+Ethanol	antagonist	116443	GRIN3A	Inhibitor
+Profenamine	antagonist	116443	GRIN3A	Inhibitor
+Ketamine	antagonist	116443	GRIN3A	Inhibitor
+Procaine	antagonist	116443	GRIN3A	Inhibitor
+Dextromethorphan	antagonist	116443	GRIN3A	Inhibitor
+Orphenadrine	antagonist	116443	GRIN3A	Inhibitor
+Amantadine	antagonist	116443	GRIN3A	Inhibitor
+Orphenadrine	antagonist	116444	GRIN3B	Inhibitor
+Irinotecan	inhibitor	116447	TOP1MT	Inhibitor
+Telotristat ethyl	antagonist	121278	TPH2	Inhibitor
+Botulinum toxin type B	unknown	127833	SYT2	other
+Amphetamine	agonist	134864	TAAR1	Activator
+Menadione	cofactor	154807	VKORC1L1	other
+Levomenthol	inducer	162514	TRPV3	Activator
+Vincristine	inhibitor	203068	TUBB	Inhibitor
+Podofilox	inhibitor	203068	TUBB	Inhibitor
+Vinblastine	binder	203068	TUBB	Ligand
+Tirbanibulin	inhibitor	203068	TUBB	Inhibitor
+Vinflunine	inhibitor	203068	TUBB	Inhibitor
+Colchicine	inhibitor|binder	203068	TUBB	Inhibitor
+Vinorelbine	antagonist|inhibitor	203068	TUBB	Activator
+Inclisiran	antisense oligonucleotide	255738	PCSK9	Activator
+Alirocumab	inhibitor	255738	PCSK9	Inhibitor
+Evolocumab	inhibitor	255738	PCSK9	Inhibitor
+Niacin	agonist	338442	HCAR2	Activator
+Fish oil	agonist|inhibits downstream inflammation cascades	338557	FFAR4	Activator
+Creatine	ligand	548596	CKMT1A	Ligand
diff --git a/drexml/resources/drugbank-v050110_mostdrugeffects_KDTs.tsv b/drexml/resources/drugbank-v050110_mostdrugeffects_KDTs.tsv
new file mode 100644
index 0000000..863a001
--- /dev/null
+++ b/drexml/resources/drugbank-v050110_mostdrugeffects_KDTs.tsv
@@ -0,0 +1,744 @@
+gene	drug_action
+ABAT	Inhibitor
+ABCA1	Inhibitor
+ABCC1	Inhibitor
+ABCC2	Inhibitor
+ABCC8	Inhibitor
+ABCC9	Activator
+ABL1	Inhibitor
+ACAT1	Inhibitor
+ACE	Inhibitor
+ACHE	Inhibitor
+ACLY	Inhibitor
+ACP3	other
+ACTN1	other
+ADA	Inhibitor
+ADH1A	Inhibitor
+ADH1B	Inhibitor
+ADH1C	Inhibitor
+ADORA1	Inhibitor
+ADORA2A	Inhibitor
+ADORA2B	Inhibitor
+ADORA3	Inhibitor
+ADRA1A	Activator
+ADRA1B	Inhibitor
+ADRA1D	Inhibitor
+ADRA2A	Activator
+ADRA2B	Activator
+ADRA2C	Activator
+ADRB1	Inhibitor
+ADRB2	Activator
+ADRB3	Activator
+AGTR1	Inhibitor
+AHR	Activator
+AKR1B1	Inhibitor
+AKR1C2	Activator
+AKR1D1	Inhibitor
+ALAD	Activator
+ALB	Ligand
+ALDH2	Inhibitor
+ALK	Inhibitor
+ALOX5	Inhibitor
+ALPG	Activator
+AMY2A	Inhibitor
+ANGPT2	Inhibitor
+ANGPTL3	Inhibitor
+ANO1	Inhibitor
+ANXA1	other
+ANXA2	Activator
+ANXA3	Activator
+ANXA4	Activator
+ANXA5	Activator
+AOC3	Inhibitor
+APOC3	Activator
+APP	Ligand
+AR	Activator
+ATIC	Inhibitor
+ATP1A1	Inhibitor
+ATP2C1	Inhibitor
+ATP4A	Inhibitor
+ATP4B	Inhibitor
+ATP6V1B2	Inhibitor
+ATP6V1C1	Inhibitor
+AVPR1A	Activator
+AVPR1B	Activator
+AVPR2	Activator
+AXL	Inhibitor
+BCHE	Inhibitor
+BCL2	Activator
+BCR	Inhibitor
+BDKRB2	Inhibitor
+BGLAP	Inhibitor
+BHMT	Activator
+BLVRB	other
+BMPR1A	Ligand
+BMPR2	Ligand
+BRAF	Inhibitor
+BST1	other
+BTK	Inhibitor
+C1R	Inhibitor
+C1S	Inhibitor
+C3	Inhibitor
+C4A	Ligand
+C4B	Ligand
+C5	Inhibitor
+C5AR1	Inhibitor
+CA1	Inhibitor
+CA12	Inhibitor
+CA14	Inhibitor
+CA2	Inhibitor
+CA3	Inhibitor
+CA4	Inhibitor
+CA7	Inhibitor
+CACNA1B	Inhibitor
+CACNA1C	Inhibitor
+CACNA1D	Inhibitor
+CACNA1E	Inhibitor
+CACNA1F	Inhibitor
+CACNA1G	Inhibitor
+CACNA1H	Inhibitor
+CACNA1I	Inhibitor
+CACNA1S	Inhibitor
+CACNA2D1	Inhibitor
+CACNA2D2	Inhibitor
+CACNB1	Inhibitor
+CACNB2	Inhibitor
+CACNB3	Inhibitor
+CACNB4	Inhibitor
+CACNG1	Inhibitor
+CALCA	Inhibitor
+CALCB	Inhibitor
+CALCR	Activator
+CALCRL	Inhibitor
+CALM1	Inhibitor
+CALM2	Inhibitor
+CALY	Inhibitor
+CAMLG	Ligand
+CARTPT	Activator
+CASP3	Inhibitor
+CASR	Activator
+CAT	Inhibitor
+CCDC51	Activator
+CCKAR	Activator
+CCKBR	Activator
+CCND1	Inhibitor
+CCR4	Inhibitor
+CCR5	Activator
+CD19	Ligand
+CD22	Inhibitor
+CD247	other
+CD274	Inhibitor
+CD33	Inhibitor
+CD3D	Activator
+CD3E	Ligand
+CD3G	other
+CD4	other
+CD44	Ligand
+CD52	Modulator
+CD79B	Modulator
+CD80	Inhibitor
+CD86	Inhibitor
+CDA	Inhibitor
+CDK4	Inhibitor
+CDK6	Inhibitor
+CFTR	Inhibitor
+CHRFAM7A	Activator
+CHRM1	Inhibitor
+CHRM2	Inhibitor
+CHRM3	Inhibitor
+CHRM4	Inhibitor
+CHRM5	Inhibitor
+CHRNA10	Inhibitor
+CHRNA2	Inhibitor
+CHRNA3	Inhibitor
+CHRNA4	Activator
+CHRNA7	Activator
+CHRNB2	Activator
+CHRNB4	Inhibitor
+CKB	Ligand
+CKM	Ligand
+CKMT1A	Ligand
+CKMT1B	Ligand
+CKMT2	Ligand
+CLCN2	Activator
+CNR1	Activator
+CNR2	Activator
+COL1A1	Ligand
+COL2A1	Ligand
+COL3A1	Ligand
+COMT	Inhibitor
+CPB2	Activator
+CPS1	Modulator
+CPT1A	Inhibitor
+CPT2	Inhibitor
+CRBN	Inhibitor
+CSF1R	Inhibitor
+CSF2RA	Activator
+CSF2RB	Activator
+CSF3R	Activator
+CTLA4	Inhibitor
+CXCR4	Activator
+CYP11A1	Inhibitor
+CYP11B1	Inhibitor
+CYP11B2	Inhibitor
+CYP17A1	Inhibitor
+CYP19A1	Inhibitor
+CYP3A4	Activator
+CYP51A1	Inhibitor
+CYSLTR1	Inhibitor
+CYSLTR2	Inhibitor
+DBH	Inhibitor
+DCK	Activator
+DDC	Inhibitor
+DDR2	Inhibitor
+DGAT2	Activator
+DHFR	Inhibitor
+DHODH	Inhibitor
+DMD	Activator
+DNMT1	Inhibitor
+DPEP1	Inhibitor
+DPP4	Inhibitor
+DPYD	Inhibitor
+DRD1	Inhibitor
+DRD2	Inhibitor
+DRD3	Activator
+DRD4	Activator
+DRD5	Activator
+ECHS1	Activator
+EDNRA	Inhibitor
+EDNRB	Inhibitor
+EEF2	Activator
+EGF	Activator
+EGFR	Inhibitor
+EGLN1	Inhibitor
+EGLN2	Inhibitor
+EGLN3	Inhibitor
+ELANE	Inhibitor
+ELOVL4	Activator
+EPAS1	Inhibitor
+EPHA2	Inhibitor
+EPOR	Activator
+ERBB2	Inhibitor
+ERBB4	Inhibitor
+ESR1	Activator
+ESR2	Activator
+ETFDH	Inhibitor
+EZH2	Inhibitor
+F10	Activator
+F11	Activator
+F12	Inhibitor
+F13A1	Activator
+F13B	Activator
+F2	Inhibitor
+F2R	Inhibitor
+F3	Activator
+F5	Activator
+F7	Ligand
+F8	Activator
+F9	Activator
+FABP7	Activator
+FADS1	Ligand
+FADS2	Ligand
+FCER1A	Activator
+FCER1G	Activator
+FCGR1A	Inhibitor
+FCGR2A	Inhibitor
+FCGR2B	Inhibitor
+FCGR3A	Activator
+FCGR3B	Inhibitor
+FCGRT	Ligand
+FDPS	Inhibitor
+FFAR1	Activator
+FFAR4	Activator
+FGA	Activator
+FGB	Activator
+FGF1	Activator
+FGF2	Activator
+FGFR1	Inhibitor
+FGFR2	Inhibitor
+FGFR3	Inhibitor
+FGFR4	Inhibitor
+FGG	Activator
+FKBP1A	Inhibitor
+FLT1	Inhibitor
+FLT3	Inhibitor
+FLT4	Inhibitor
+FN1	Inhibitor
+FNTA	Inhibitor
+FNTB	Inhibitor
+FOLH1	Ligand
+FOLR1	Ligand
+FPGS	Activator
+FRK	Inhibitor
+FSHR	Activator
+FTH1	Ligand
+FTL	Ligand
+FXYD2	Inhibitor
+GAA	Inhibitor
+GABBR1	Activator
+GABBR2	Activator
+GABRA1	Activator
+GABRA2	Activator
+GABRA3	Activator
+GABRA4	Activator
+GABRA5	Activator
+GABRA6	Activator
+GABRB1	Activator
+GABRB2	Activator
+GABRB3	Activator
+GABRG2	Inhibitor
+GAMT	other
+GANAB	Inhibitor
+GANC	Inhibitor
+GART	Inhibitor
+GAST	Inhibitor
+GCGR	Activator
+GGCX	Activator
+GGPS1	Inhibitor
+GHR	Ligand
+GHRHR	Ligand
+GIP	Activator
+GLP1R	Activator
+GLP2R	Activator
+GLRA1	Activator
+GLRA2	Activator
+GLRA3	Activator
+GNRHR	Inhibitor
+GRIA1	Inhibitor
+GRIN1	Inhibitor
+GRIN2A	Inhibitor
+GRIN2B	Inhibitor
+GRIN2D	Inhibitor
+GRIN3A	Inhibitor
+GRIN3B	Inhibitor
+GRM5	Inhibitor
+GSR	Inhibitor
+GSS	Activator
+GUCY1A2	Activator
+GUCY1B1	Activator
+GUCY2C	Activator
+HADH	Activator
+HAO1	Activator
+HBA1	Ligand
+HBA2	Ligand
+HBB	Activator
+HCAR2	Activator
+HCAR3	Activator
+HCRTR1	Inhibitor
+HCRTR2	Inhibitor
+HDAC1	Activator
+HDAC2	Activator
+HDAC3	Inhibitor
+HDAC6	Inhibitor
+HDAC9	Inhibitor
+HMGCR	Inhibitor
+HMMR	Ligand
+HPD	Inhibitor
+HPRT1	Inhibitor
+HRH1	Inhibitor
+HRH2	Inhibitor
+HRH3	Activator
+HRH4	Modulator
+HSD11B1	Inhibitor
+HSD17B10	Activator
+HTR1A	Activator
+HTR1B	Activator
+HTR1D	Activator
+HTR1F	Activator
+HTR2A	Inhibitor
+HTR2B	Inhibitor
+HTR2C	Inhibitor
+HTR3A	Inhibitor
+HTR4	Activator
+HTR7	Inhibitor
+ICAM1	Inhibitor
+IDE	Inhibitor
+IDH1	Inhibitor
+IDH2	Inhibitor
+IFNAR1	Activator
+IFNAR2	Activator
+IFNG	other
+IFNGR1	Ligand
+IFNGR2	Ligand
+IGF1R	Activator
+IKBKB	Activator
+IL11RA	Activator
+IL12B	Inhibitor
+IL13	Inhibitor
+IL17A	Modulator
+IL17F	Modulator
+IL1B	Activator
+IL1R1	Inhibitor
+IL1RL2	Inhibitor
+IL23A	Inhibitor
+IL2RA	Activator
+IL2RB	Activator
+IL2RG	Activator
+IL3RA	Activator
+IL4	Inhibitor
+IL4R	Inhibitor
+IL5	Activator
+IL5RA	Modulator
+IL6	Activator
+IL6R	Inhibitor
+IMPDH1	Inhibitor
+IMPDH2	Inhibitor
+INSR	Activator
+ITGA2B	Inhibitor
+ITGA4	Modulator
+ITGAL	Inhibitor
+ITGAV	other
+ITGB1	other
+ITGB3	other
+ITGB7	Modulator
+JAK1	Inhibitor
+JAK2	Inhibitor
+JAK3	Inhibitor
+JUN	Activator
+KCNA1	Activator
+KCNA10	Inhibitor
+KCNA2	Inhibitor
+KCNA3	Inhibitor
+KCNA4	Inhibitor
+KCNA5	Inhibitor
+KCNA6	Inhibitor
+KCNA7	Inhibitor
+KCNB1	Inhibitor
+KCNB2	Inhibitor
+KCNC1	Inhibitor
+KCNC2	Inhibitor
+KCNC3	Inhibitor
+KCND1	Inhibitor
+KCND2	Inhibitor
+KCND3	Inhibitor
+KCNH2	Inhibitor
+KCNJ1	Inhibitor
+KCNJ11	Activator
+KCNJ12	Inhibitor
+KCNJ3	Inhibitor
+KCNJ8	Inhibitor
+KCNK2	Inhibitor
+KCNK3	Inhibitor
+KCNK9	Inhibitor
+KCNMA1	Activator
+KCNN4	Inhibitor
+KCNQ1	Inhibitor
+KDR	Inhibitor
+KEAP1	Ligand
+KIT	Inhibitor
+KLK3	Ligand
+KLKB1	Inhibitor
+KRAS	Inhibitor
+KRT12	Inhibitor
+KRT2	Inhibitor
+LAG3	Modulator
+LCK	Inhibitor
+LDHA	Inhibitor
+LDHB	Inhibitor
+LEPR	Activator
+LHCGR	other
+LIPF	Inhibitor
+LPL	Activator
+LTA	Modulator
+M6PR	Ligand
+MAOA	Inhibitor
+MAOB	Inhibitor
+MAP1A	Inhibitor
+MAP2	other
+MAP2K1	Inhibitor
+MAP2K2	Inhibitor
+MAP3K1	Inhibitor
+MAP4	other
+MAPK1	Activator
+MAPK11	Inhibitor
+MAPK3	Activator
+MAPT	other
+MB	Inhibitor
+MC1R	Activator
+MC2R	Activator
+MC4R	Activator
+MET	Inhibitor
+MGAM	Activator
+MME	Activator
+MMUT	other
+MOCS2	Activator
+MPL	Activator
+MR1	Inhibitor
+MRC1	Ligand
+MS4A1	Inhibitor
+MS4A2	Inhibitor
+MST1R	Inhibitor
+MTNR1A	Activator
+MTNR1B	Activator
+MTOR	Inhibitor
+MTR	other
+MTTP	Inhibitor
+MYH7	Inhibitor
+ND1	Inhibitor
+NDUFV3	other
+NEU1	Inhibitor
+NFKB1	Inhibitor
+NISCH	Activator
+NNMT	Ligand
+NOD2	Ligand
+NOS1	Inhibitor
+NOS2	Activator
+NOS3	other
+NPC1L1	Inhibitor
+NPR1	Activator
+NPR2	Activator
+NPY	Inhibitor
+NR1H4	Activator
+NR1I2	Activator
+NR3C1	Activator
+NR3C2	Inhibitor
+NTRK1	Inhibitor
+NTRK2	Inhibitor
+NTRK3	Inhibitor
+NTSR2	Activator
+OPRD1	Activator
+OPRK1	Activator
+OPRM1	Activator
+OXTR	Activator
+P2RX4	Inhibitor
+P2RY12	Inhibitor
+PAH	other
+PARP1	Inhibitor
+PARP2	Inhibitor
+PARP3	Inhibitor
+PCSK9	Inhibitor
+PDCD1	Inhibitor
+PDE10A	Inhibitor
+PDE3A	Inhibitor
+PDE4A	Inhibitor
+PDE4B	Inhibitor
+PDE4C	Inhibitor
+PDE4D	Inhibitor
+PDE5A	Inhibitor
+PDGFRA	Inhibitor
+PDGFRB	Inhibitor
+PDK1	Inhibitor
+PDXK	Ligand
+PGA5	Inhibitor
+PGF	Ligand
+PGR	Activator
+PIK3CA	Inhibitor
+PIK3CD	Inhibitor
+PIK3CG	Inhibitor
+PKLR	Activator
+PLA2G10	Inhibitor
+PLA2G1B	Inhibitor
+PLA2G2A	Inhibitor
+PLAT	Inhibitor
+PLG	Activator
+PLIN3	other
+PMEL	Ligand
+PNLIP	Inhibitor
+PNP	Activator
+POLA1	Inhibitor
+POLB	Inhibitor
+POLE	Inhibitor
+POLE2	Inhibitor
+POLE3	Inhibitor
+POLE4	Inhibitor
+PPARA	Activator
+PPARD	Activator
+PPARG	Activator
+PPIA	Inhibitor
+PPIF	Ligand
+PPP3R1	Inhibitor
+PPP3R2	Inhibitor
+PRDX5	Inhibitor
+PRKAB1	Activator
+PRKCA	Activator
+PRLR	Ligand
+PROC	Inhibitor
+PROCR	Inhibitor
+PROS1	Inhibitor
+PSMB1	Inhibitor
+PSMB10	Inhibitor
+PSMB2	Inhibitor
+PSMB5	Inhibitor
+PSMB8	Inhibitor
+PSMB9	Inhibitor
+PTAFR	Inhibitor
+PTGDR	Activator
+PTGER1	Activator
+PTGER2	Activator
+PTGER3	Activator
+PTGER4	Activator
+PTGFR	Activator
+PTGIR	Activator
+PTGIS	Activator
+PTGS1	Inhibitor
+PTGS2	Inhibitor
+PTH1R	Ligand
+PTH2R	Activator
+QPRT	Ligand
+RAF1	Inhibitor
+RAMP1	Activator
+RAMP2	Activator
+RAMP3	Activator
+RARA	Activator
+RARB	Activator
+RARG	Activator
+REN	Inhibitor
+RET	Inhibitor
+RFK	Ligand
+ROCK1	Inhibitor
+ROCK2	Inhibitor
+ROS1	Inhibitor
+RPE65	other
+RPL3	Inhibitor
+RRM1	Inhibitor
+RRM2	Inhibitor
+RRM2B	Inhibitor
+RXRA	Activator
+RXRB	Activator
+RXRG	Activator
+RYR1	Inhibitor
+RYR2	Inhibitor
+S1PR1	Activator
+S1PR3	Inhibitor
+S1PR5	Activator
+SCN10A	Inhibitor
+SCN11A	Inhibitor
+SCN1A	Inhibitor
+SCN1B	Inhibitor
+SCN2A	Inhibitor
+SCN2B	Inhibitor
+SCN3A	Inhibitor
+SCN3B	Inhibitor
+SCN4A	Inhibitor
+SCN4B	Inhibitor
+SCN5A	Inhibitor
+SCN8A	Inhibitor
+SCN9A	Inhibitor
+SCNN1A	Inhibitor
+SCNN1B	Inhibitor
+SCNN1D	Inhibitor
+SCNN1G	Inhibitor
+SCTR	Ligand
+SDHA	other
+SELP	Inhibitor
+SERPINC1	Activator
+SERPIND1	Activator
+SHBG	Activator
+SI	Inhibitor
+SIGMAR1	Activator
+SLAMF7	Inhibitor
+SLC10A1	Inhibitor
+SLC10A2	Inhibitor
+SLC12A1	Inhibitor
+SLC12A2	Inhibitor
+SLC12A3	Inhibitor
+SLC12A4	Inhibitor
+SLC12A5	Inhibitor
+SLC18A2	Inhibitor
+SLC1A5	Ligand
+SLC22A11	Inhibitor
+SLC22A12	Inhibitor
+SLC22A6	Inhibitor
+SLC22A8	Inhibitor
+SLC25A4	Inhibitor
+SLC25A5	Inhibitor
+SLC25A6	Inhibitor
+SLC52A2	Activator
+SLC5A1	Inhibitor
+SLC5A2	Inhibitor
+SLC5A5	Activator
+SLC6A1	Inhibitor
+SLC6A2	Inhibitor
+SLC6A3	Inhibitor
+SLC6A4	Inhibitor
+SLC6A8	other
+SLC7A11	Activator
+SLC7A3	Ligand
+SLC7A7	Ligand
+SLC8A1	Inhibitor
+SLC9A3	Inhibitor
+SMN1	Activator
+SMO	Inhibitor
+SNAP25	Inhibitor
+SOAT1	Inhibitor
+SOST	Inhibitor
+SQLE	Inhibitor
+SRC	Inhibitor
+SRD5A1	Inhibitor
+SRD5A2	Inhibitor
+SSTR1	Activator
+SSTR2	Ligand
+SSTR3	Activator
+SSTR4	Activator
+SSTR5	Activator
+SV2A	Activator
+SYK	Inhibitor
+SYT2	other
+TAAR1	Activator
+TACR1	Inhibitor
+TACSTD2	Modulator
+TEK	Inhibitor
+TFPI	Inhibitor
+TFRC	Activator
+TGFB1	Activator
+TGFBR2	Activator
+TH	Ligand
+THRA	Activator
+THRB	Activator
+TK2	Activator
+TLR2	Inhibitor
+TLR4	Ligand
+TLR7	Activator
+TLR9	Inhibitor
+TNF	Inhibitor
+TNFRSF17	Ligand
+TNFRSF1A	Activator
+TNFRSF8	Inhibitor
+TNFSF11	Inhibitor
+TNFSF13B	Modulator
+TNNC1	Activator
+TOP1	Inhibitor
+TOP1MT	Inhibitor
+TOP2A	Inhibitor
+TOP2B	Inhibitor
+TPH1	Inhibitor
+TPH2	Inhibitor
+TPK1	Activator
+TPMT	Inhibitor
+TPO	Inhibitor
+TRHR	Ligand
+TRPA1	Activator
+TRPM7	Inhibitor
+TRPM8	Activator
+TRPV1	Activator
+TRPV3	Activator
+TSHR	Activator
+TSLP	Inhibitor
+TTR	other
+TUBA1A	Inhibitor
+TUBA4A	Inhibitor
+TUBB	Inhibitor
+TUBB1	Inhibitor
+TUBB3	Inhibitor
+TUBB4B	Inhibitor
+TUBD1	Ligand
+TUBE1	Ligand
+TUBG1	Ligand
+TXNRD1	Inhibitor
+TYK2	Inhibitor
+TYMP	Inhibitor
+TYMS	Inhibitor
+TYR	Inhibitor
+UGCG	Inhibitor
+VAMP1	Ligand
+VAMP2	Ligand
+VDR	Activator
+VEGFA	Inhibitor
+VEGFB	Ligand
+VKORC1	Inhibitor
+VKORC1L1	other
+VWF	Ligand
+XDH	Inhibitor
+XIAP	Activator
+XPO1	Inhibitor
+YES1	Inhibitor
diff --git a/drexml/shap_scores_filtering.py b/drexml/shap_scores_filtering.py
new file mode 100644
index 0000000..bbfb126
--- /dev/null
+++ b/drexml/shap_scores_filtering.py
@@ -0,0 +1,42 @@
+# -*- coding: utf-8 -*-
+"""
+SHAP scores analysis for DREXML.
+"""
+import pandas as pd
+
+def process_data(data_folder, stability_threshold = 0.6):
+    """
+    Once the folder where the model results is defined, this function reads the shap_summary (scores) and shap_selection (selected as relevant) .tsv     files into DataFrames, applies Min-Max normalization to make the scores comparable, filters the circuits 
+    based on a desired stability threshold (by default 0.6), and saves the final shap_filtered_stable dataframe to a .tsv file.
+
+    Args:
+    data_folder (str): The path to the data folder.
+    stability_threshold (float): The stability threshold to be set (we recommend using a value between 0.4-0.7)
+ 
+    Returns:
+    The final dataframe is saved as a .tsv file.
+    """
+    # Read the .tsv files into a DataFrames
+    shap_values = pd.read_csv(f"{data_folder}/shap_summary_symbol.tsv", sep='\t', index_col=0)
+    shap_selection = pd.read_csv(f"{data_folder}/shap_selection_symbol.tsv", sep='\t', index_col=0)
+    stability = pd.read_csv(f"{data_folder}/stability_results_symbol.tsv", sep='\t', index_col=0) 
+
+    # Define min and max range for normalization
+    min_range = -1
+    max_range = 1
+
+    # Apply Min-Max normalization to each row
+    shap_normalized = shap_values.copy()
+    shap_normalized.iloc[:,1:] = shap_normalized.iloc[:,1:].apply(lambda row: (row - row.min()) / (row.max() - row.min()) * (max_range - min_range) + min_range, axis=1)
+
+    shap_filtered_norm = shap_normalized * shap_selection
+    filtered_df = shap_filtered_norm.loc[:, (shap_filtered_norm != 0).sum() != 0]
+
+    # Now filter those circuits that have stability above the desired threshold
+    stability_00 = stability[stability['stability'] > stability_threshold]
+
+    stability_00.reset_index(inplace=True)
+    filtered_df.reset_index(inplace=True)
+
+    filtered_df_stable = filtered_df[filtered_df['circuit_name'].isin(stability_00['circuit_name'])]
+    filtered_df_stable.to_csv(f"{data_folder}/shap_filtered_stability_symbol.tsv", sep='\t', index=False)
\ No newline at end of file

From 296604b43a1c1d6a88e6f77230a4d643588222f4 Mon Sep 17 00:00:00 2001
From: marinaEM <marina.estebanm@gmail.com>
Date: Fri, 28 Jul 2023 00:26:52 +0200
Subject: [PATCH 04/37] Addition of ORA analysis module for relevant selected
 drugs

---
 drexml/ORA_drugs.py | 123 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 123 insertions(+)
 create mode 100644 drexml/ORA_drugs.py

diff --git a/drexml/ORA_drugs.py b/drexml/ORA_drugs.py
new file mode 100644
index 0000000..927da8c
--- /dev/null
+++ b/drexml/ORA_drugs.py
@@ -0,0 +1,123 @@
+# -*- coding: utf-8 -*-
+"""
+Over representation analysis of ATC classifiction across levels for SHAP selected drugs.
+"""
+
+import pathlib
+import shutil
+import urllib.request
+
+import dotenv
+import numpy as np
+import pandas as pd
+from scipy import stats
+from statsmodels.stats.multitest import multipletests
+
+def fdr(pvalues):
+    """
+    Function to apply Benjamini-Hochberg FDR p-value correction for multiple hypothesis testing.
+
+    Args:
+    pvalues (array-like): array of p-values to correct.
+
+    Returns:
+    array: corrected p-values.
+    """
+    return multipletests(pvalues, alpha=0.05, method="fdr_bh")[1]
+
+
+def main(db_path, atc_path):
+    """
+    Main function that reads drugbank data and ATC codes, applies over-representation analysis
+    on selected drugs at different ATC levels, and saves the results.
+
+    Args:
+    db_path (str): Path to the drugbank data file.
+    atc_path (str): Path to the ATC codes file.
+
+    Returns:
+    None
+    """
+    db_path = pathlib.Path(db_path)
+    atc_path = pathlib.Path(atc_path)
+    np.random.seed(42)
+
+    project_root = pathlib.Path(dotenv.find_dotenv()).absolute().parent
+    data_folder = project_root.joinpath("data")
+    raw_folder = data_folder.joinpath("raw")
+    final_folder = data_folder.joinpath("final")
+    results_folder = project_root.joinpath("results")
+    tables_folder = results_folder.joinpath("tables")
+    tables_folder.mkdir(parents=True, exist_ok=True)
+
+    atc_url = "https://raw.githubusercontent.com/fabkury/atcd/master/WHO%20ATC-DDD%202021-12-03.csv"
+    
+    # If ATC codes file doesn't exist, download it from the internet
+    if not atc_path.exists():
+        with urllib.request.urlopen(atc_url) as response, open(atc_path, "wb") as out_file:
+            shutil.copyfileobj(response, out_file)
+
+    atc_code_name = pd.read_csv(atc_path, usecols=["atc_code", "atc_name"])
+
+    # Read data files
+    shap_selection_df = pd.read_csv(
+        results_folder.joinpath("ml", "shap_selection_symbol.tsv"), sep="\t", index_col=0
+    )
+    drugbank_df = pd.read_csv(
+        final_folder.joinpath(db_path), sep="\t"
+    ).assign(
+        is_selected=lambda x: x.symbol_id.isin(
+            shap_selection_df.columns[shap_selection_df.any()]
+        )
+    )
+
+    atc_level_to_len = {1: 1, 2: 3, 3: 4, 4: 5}
+
+    results = []
+    ora_min_len = 3
+
+    # Apply over-representation analysis on selected drugs at different ATC levels
+    for atc_level, level_len in atc_level_to_len.items():
+        # Preprocess the data for ORA
+        tmp_df = (
+            drugbank_df.loc[:, ["drugbank_id", "atc_codes", "is_selected"]]
+            .dropna()
+            .drop_duplicates()
+            .groupby("drugbank_id")
+            .agg({"atc_codes": list, "is_selected": "any"})
+            .reset_index()
+            .explode("atc_codes")
+            .assign(atc_codes=lambda x: x.atc_codes.str.split("|"))
+            .explode("atc_codes")
+            .assign(atc_codes=lambda x: x.atc_codes.str[:level_len])
+            .drop_duplicates()
+        )
+        atc_dict = (
+            tmp_df[["drugbank_id", "atc_codes"]]
+            .groupby("atc_codes")
+            .agg({"drugbank_id": list})
+            .to_dict()["drugbank_id"]
+        )
+        background = tmp_df.drugbank_id.unique()
+        drugs_selected = tmp_df.drugbank_id[tmp_df.is_selected].unique()
+
+        ora_dict = {}
+
+        # Apply ORA
+        for atc_code in atc_dict.keys():
+            drug_list_in_atc = np.unique(atc_dict[atc_code])
+            n_drug_list_in_atc = drug_list_in_atc.size
+            if n_drug_list_in_atc < ora_min_len:
+                print(f"Ignore ATC codes with less than {ora_min_len} drugs")
+            else:
+                selected_drugs_in_atc = np.intersect1d(drugs_selected, drug_list_in_atc)
+                selected_drugs_notin_atc = np.setdiff1d(drugs_selected, drug_list_in_atc)
+
+                drugs_in_atc_not_selected = np.intersect1d(
+                    drug_list_in_atc, np.setdiff1d(background, drugs_selected)
+                )
+                drugs_notin_atc_not_selected = np.setdiff1d(
+                    np.setdiff1d(background, drugs_selected), drug_list_in_atc
+                )
+
+                contingency_table = np
\ No newline at end of file

From dc7d08a45265b1ad5c8452d5082655b00c0400c0 Mon Sep 17 00:00:00 2001
From: marinaEM <marina.estebanm@gmail.com>
Date: Fri, 28 Jul 2023 18:35:47 +0200
Subject: [PATCH 05/37] Fixing bug from very large results matrix for better
 visualizations

---
 drexml/boxplots_shap_scores.py   |   22 +-
 drexml/heatmap_plot.ipynb        | 1314 ------------------------------
 drexml/heatmap_shap_scores.py    |   46 +-
 drexml/heatmap_top10KDT_drugs.py |   57 +-
 drexml/histograms_shap_scores.py |   20 +-
 drexml/shap_scores_filtering.py  |    4 +-
 6 files changed, 108 insertions(+), 1355 deletions(-)
 delete mode 100644 drexml/heatmap_plot.ipynb

diff --git a/drexml/boxplots_shap_scores.py b/drexml/boxplots_shap_scores.py
index 006490c..6bf909f 100644
--- a/drexml/boxplots_shap_scores.py
+++ b/drexml/boxplots_shap_scores.py
@@ -9,19 +9,15 @@
 
 def create_boxplots(data_folder, filtered_file_name):
     """
-    This function reads the filtered data, creates boxplots for all score columns, and saves the boxplots to a .pdf file.
+    This function reads the filtered data, creates boxplots of the scores from the relevant drug-targets on the disease circuits, and saves the boxplots to a .pdf file.
 
     Args:
     data_folder (str): The path to the data folder.
     filtered_file_name (str): The name of the shap scores filtered file.
 
     Returns:
-    The boxplots are saved as a .pdf file.
+    None. The boxplots are saved as a .pdf file.
     """
-    import pandas as pd
-    import matplotlib.pyplot as plt
-    import seaborn as sns
-
     # Load the previous filtered table
     data = pd.read_csv(f"{data_folder}/{filtered_file_name}", sep='\t', index_col=0)
 
@@ -34,16 +30,24 @@ def create_boxplots(data_folder, filtered_file_name):
 
     # Order the data based on medians
     melted_data["variable"] = pd.Categorical(melted_data["variable"], categories=medians.index, ordered=True)
+    
+    # Calculate the width size based on the number of variables
+    num_variables = melted_data["variable"].nunique()
+    fig_width = max(10, num_variables / 2)  # adjust this formula as needed
+    
 
     # Create the boxplots
     plt.rcParams['font.family'] = 'DejaVu Sans'
-    plt.figure(figsize=(20, 12), facecolor='white')
+    plt.figure(figsize=(fig_width, 12), facecolor='white')
     sns.set_style("whitegrid")  # set Seaborn style to white grid
-    sns.boxplot(x="variable", y="value", data = melted_data)
+    boxplot = sns.boxplot(x="variable", y="value", data = melted_data)
+    # Set the font size for x tick labels
+    # boxplot.set_xticklabels(boxplot.get_xticklabels(), size=15)
+    
     plt.xticks(rotation=40, ha = "right")
     plt.title("Boxplots of SHAP-Scores")
     plt.ylabel('SHAP Scores')
     plt.xlabel('Drug-target')
     plt.tight_layout()
     plt.savefig(f"{data_folder}/relevant_drugTargetscores_boxplots.pdf", dpi=300)
-    plt.show()
\ No newline at end of file
+    plt.show()
diff --git a/drexml/heatmap_plot.ipynb b/drexml/heatmap_plot.ipynb
deleted file mode 100644
index c362dec..0000000
--- a/drexml/heatmap_plot.ipynb
+++ /dev/null
@@ -1,1314 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 168,
-   "id": "12763c3b-3a9a-4340-9fc5-f3e858c26da6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%reset -f\n",
-    "\n",
-    "import pandas as pd\n",
-    "\n",
-    "data_folder= \"/home/m3m/INFO_PROYECTO/drexml_paper_paquete/results/Fanconi_Anemia/results\"\n",
-    "\n",
-    "# Read the .tsv files into a DataFrames\n",
-    "shap_values = pd.read_csv(f\"{data_folder}/shap_summary_symbol.tsv\", sep='\\t', index_col=0)\n",
-    "shap_selection = pd.read_csv(f\"{data_folder}/shap_selection_symbol.tsv\", sep='\\t', index_col=0)\n",
-    "stability = pd.read_csv(f\"{data_folder}/stability_results_symbol.tsv\", sep='\\t', index_col=0) \n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 169,
-   "id": "8a370d44-b8af-4d65-bfdd-5ff39856adb3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ABAT</th>\n",
-       "      <th>ABCA1</th>\n",
-       "      <th>ABL1</th>\n",
-       "      <th>ACAT1</th>\n",
-       "      <th>ACHE</th>\n",
-       "      <th>ACLY</th>\n",
-       "      <th>ACP3</th>\n",
-       "      <th>ACTN1</th>\n",
-       "      <th>ADA</th>\n",
-       "      <th>ADH1A</th>\n",
-       "      <th>...</th>\n",
-       "      <th>TPH2</th>\n",
-       "      <th>SYT2</th>\n",
-       "      <th>TAAR1</th>\n",
-       "      <th>VKORC1L1</th>\n",
-       "      <th>TRPV3</th>\n",
-       "      <th>TUBB</th>\n",
-       "      <th>PCSK9</th>\n",
-       "      <th>HCAR2</th>\n",
-       "      <th>FFAR4</th>\n",
-       "      <th>CKMT1A</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>circuit_name</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Fanconi anemia pathway: RAD51</th>\n",
-       "      <td>-8.689657e-08</td>\n",
-       "      <td>-0.386536</td>\n",
-       "      <td>-0.401960</td>\n",
-       "      <td>-0.396785</td>\n",
-       "      <td>-0.521874</td>\n",
-       "      <td>-0.378718</td>\n",
-       "      <td>-0.300026</td>\n",
-       "      <td>-0.379242</td>\n",
-       "      <td>-0.389304</td>\n",
-       "      <td>-0.389326</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.388704</td>\n",
-       "      <td>-0.389426</td>\n",
-       "      <td>-0.388067</td>\n",
-       "      <td>-0.384761</td>\n",
-       "      <td>-0.384756</td>\n",
-       "      <td>-0.357535</td>\n",
-       "      <td>-0.372284</td>\n",
-       "      <td>-0.359548</td>\n",
-       "      <td>-0.365995</td>\n",
-       "      <td>-0.403467</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Fanconi anemia pathway: MLH1 PMS2</th>\n",
-       "      <td>-8.077751e-08</td>\n",
-       "      <td>-0.238399</td>\n",
-       "      <td>-0.269825</td>\n",
-       "      <td>-0.260549</td>\n",
-       "      <td>-0.431095</td>\n",
-       "      <td>-0.230148</td>\n",
-       "      <td>-0.084805</td>\n",
-       "      <td>-0.233656</td>\n",
-       "      <td>-0.252828</td>\n",
-       "      <td>-0.241303</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.242601</td>\n",
-       "      <td>-0.241099</td>\n",
-       "      <td>-0.243983</td>\n",
-       "      <td>-0.235437</td>\n",
-       "      <td>-0.234357</td>\n",
-       "      <td>-0.267757</td>\n",
-       "      <td>-0.214188</td>\n",
-       "      <td>-0.200782</td>\n",
-       "      <td>-0.200288</td>\n",
-       "      <td>-0.237795</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Fanconi anemia pathway: RAD51C</th>\n",
-       "      <td>-7.470089e-08</td>\n",
-       "      <td>-0.414730</td>\n",
-       "      <td>-0.440035</td>\n",
-       "      <td>-0.430456</td>\n",
-       "      <td>-0.632182</td>\n",
-       "      <td>-0.405163</td>\n",
-       "      <td>-0.326664</td>\n",
-       "      <td>-0.404697</td>\n",
-       "      <td>-0.420671</td>\n",
-       "      <td>-0.419098</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.418211</td>\n",
-       "      <td>-0.415957</td>\n",
-       "      <td>-0.417600</td>\n",
-       "      <td>-0.409293</td>\n",
-       "      <td>-0.412086</td>\n",
-       "      <td>-0.394902</td>\n",
-       "      <td>-0.401005</td>\n",
-       "      <td>-0.384553</td>\n",
-       "      <td>-0.369725</td>\n",
-       "      <td>-0.432237</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Fanconi anemia pathway: FANCI</th>\n",
-       "      <td>-1.061490e-07</td>\n",
-       "      <td>0.003305</td>\n",
-       "      <td>0.027404</td>\n",
-       "      <td>-0.015537</td>\n",
-       "      <td>-0.526704</td>\n",
-       "      <td>0.027307</td>\n",
-       "      <td>0.162895</td>\n",
-       "      <td>0.038285</td>\n",
-       "      <td>0.005416</td>\n",
-       "      <td>0.004532</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.009642</td>\n",
-       "      <td>0.010136</td>\n",
-       "      <td>0.007968</td>\n",
-       "      <td>0.025313</td>\n",
-       "      <td>0.013168</td>\n",
-       "      <td>-0.008716</td>\n",
-       "      <td>0.022125</td>\n",
-       "      <td>0.047733</td>\n",
-       "      <td>0.059667</td>\n",
-       "      <td>-0.011232</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Fanconi anemia pathway: BRCA1</th>\n",
-       "      <td>-1.683542e-05</td>\n",
-       "      <td>-0.301273</td>\n",
-       "      <td>-0.283741</td>\n",
-       "      <td>-0.310443</td>\n",
-       "      <td>-0.621120</td>\n",
-       "      <td>-0.270129</td>\n",
-       "      <td>-0.226952</td>\n",
-       "      <td>-0.287605</td>\n",
-       "      <td>-0.293040</td>\n",
-       "      <td>-0.300574</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.298489</td>\n",
-       "      <td>-0.299828</td>\n",
-       "      <td>-0.297885</td>\n",
-       "      <td>-0.286508</td>\n",
-       "      <td>-0.294695</td>\n",
-       "      <td>-0.219823</td>\n",
-       "      <td>-0.307477</td>\n",
-       "      <td>-0.282670</td>\n",
-       "      <td>-0.263800</td>\n",
-       "      <td>-0.323152</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 743 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                           ABAT     ABCA1      ABL1     ACAT1  \\\n",
-       "circuit_name                                                                    \n",
-       "Fanconi anemia pathway: RAD51     -8.689657e-08 -0.386536 -0.401960 -0.396785   \n",
-       "Fanconi anemia pathway: MLH1 PMS2 -8.077751e-08 -0.238399 -0.269825 -0.260549   \n",
-       "Fanconi anemia pathway: RAD51C    -7.470089e-08 -0.414730 -0.440035 -0.430456   \n",
-       "Fanconi anemia pathway: FANCI     -1.061490e-07  0.003305  0.027404 -0.015537   \n",
-       "Fanconi anemia pathway: BRCA1     -1.683542e-05 -0.301273 -0.283741 -0.310443   \n",
-       "\n",
-       "                                       ACHE      ACLY      ACP3     ACTN1  \\\n",
-       "circuit_name                                                                \n",
-       "Fanconi anemia pathway: RAD51     -0.521874 -0.378718 -0.300026 -0.379242   \n",
-       "Fanconi anemia pathway: MLH1 PMS2 -0.431095 -0.230148 -0.084805 -0.233656   \n",
-       "Fanconi anemia pathway: RAD51C    -0.632182 -0.405163 -0.326664 -0.404697   \n",
-       "Fanconi anemia pathway: FANCI     -0.526704  0.027307  0.162895  0.038285   \n",
-       "Fanconi anemia pathway: BRCA1     -0.621120 -0.270129 -0.226952 -0.287605   \n",
-       "\n",
-       "                                        ADA     ADH1A  ...      TPH2  \\\n",
-       "circuit_name                                           ...             \n",
-       "Fanconi anemia pathway: RAD51     -0.389304 -0.389326  ... -0.388704   \n",
-       "Fanconi anemia pathway: MLH1 PMS2 -0.252828 -0.241303  ... -0.242601   \n",
-       "Fanconi anemia pathway: RAD51C    -0.420671 -0.419098  ... -0.418211   \n",
-       "Fanconi anemia pathway: FANCI      0.005416  0.004532  ...  0.009642   \n",
-       "Fanconi anemia pathway: BRCA1     -0.293040 -0.300574  ... -0.298489   \n",
-       "\n",
-       "                                       SYT2     TAAR1  VKORC1L1     TRPV3  \\\n",
-       "circuit_name                                                                \n",
-       "Fanconi anemia pathway: RAD51     -0.389426 -0.388067 -0.384761 -0.384756   \n",
-       "Fanconi anemia pathway: MLH1 PMS2 -0.241099 -0.243983 -0.235437 -0.234357   \n",
-       "Fanconi anemia pathway: RAD51C    -0.415957 -0.417600 -0.409293 -0.412086   \n",
-       "Fanconi anemia pathway: FANCI      0.010136  0.007968  0.025313  0.013168   \n",
-       "Fanconi anemia pathway: BRCA1     -0.299828 -0.297885 -0.286508 -0.294695   \n",
-       "\n",
-       "                                       TUBB     PCSK9     HCAR2     FFAR4  \\\n",
-       "circuit_name                                                                \n",
-       "Fanconi anemia pathway: RAD51     -0.357535 -0.372284 -0.359548 -0.365995   \n",
-       "Fanconi anemia pathway: MLH1 PMS2 -0.267757 -0.214188 -0.200782 -0.200288   \n",
-       "Fanconi anemia pathway: RAD51C    -0.394902 -0.401005 -0.384553 -0.369725   \n",
-       "Fanconi anemia pathway: FANCI     -0.008716  0.022125  0.047733  0.059667   \n",
-       "Fanconi anemia pathway: BRCA1     -0.219823 -0.307477 -0.282670 -0.263800   \n",
-       "\n",
-       "                                     CKMT1A  \n",
-       "circuit_name                                 \n",
-       "Fanconi anemia pathway: RAD51     -0.403467  \n",
-       "Fanconi anemia pathway: MLH1 PMS2 -0.237795  \n",
-       "Fanconi anemia pathway: RAD51C    -0.432237  \n",
-       "Fanconi anemia pathway: FANCI     -0.011232  \n",
-       "Fanconi anemia pathway: BRCA1     -0.323152  \n",
-       "\n",
-       "[5 rows x 743 columns]"
-      ]
-     },
-     "execution_count": 169,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Define min and max range for normalization\n",
-    "min_range = -1\n",
-    "max_range = 1\n",
-    "\n",
-    "# Apply Min-Max normalization to each row\n",
-    "shap_normalized = shap_values.copy()\n",
-    "shap_normalized.iloc[:,1:] = shap_normalized.iloc[:,1:].apply(lambda row: (row - row.min()) / (row.max() - row.min()) * (max_range - min_range) + min_range, axis=1)\n",
-    "\n",
-    "shap_normalized.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 170,
-   "id": "215d6e5b-b832-4a5e-b646-ded14c3cfed8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "shap_filtered_norm = shap_normalized * shap_selection\n",
-    "\n",
-    "filtered_df = shap_filtered_norm.loc[:, (shap_filtered_norm != 0).sum() != 0]\n",
-    "\n",
-    "filtered_df.head()\n",
-    "\n",
-    "filtered_df.to_csv(f\"{data_folder}/shap_filtered_symbol.tsv\", sep='\\t', index=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 171,
-   "id": "621445ac-cd6c-4b4c-b39b-82b116cdf1f6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                        circuit_name      ACHE   ADORA2A  \\\n",
-      "0                      Fanconi anemia pathway: RAD51 -0.521874 -0.000000   \n",
-      "2                     Fanconi anemia pathway: RAD51C -0.000000 -0.000000   \n",
-      "3                      Fanconi anemia pathway: FANCI -0.526704 -0.000000   \n",
-      "4                      Fanconi anemia pathway: BRCA1 -0.621120 -0.000000   \n",
-      "5             Fanconi anemia pathway: FANCM C19orf40 -0.644022 -0.457955   \n",
-      "6               Fanconi anemia pathway: FANCM STRA13 -0.690006 -0.546230   \n",
-      "8                 Cell cycle: SMC1B SMC3 STAG1 RAD21  0.000000  0.000000   \n",
-      "9                              Oocyte meiosis: REC8*  0.542976  0.508633   \n",
-      "10  Progesterone-mediated oocyte maturation: ANAPC10  0.000000  0.000000   \n",
-      "\n",
-      "         AHR   SLC25A4     ANXA2     ANXA4  CACNB1    COL3A1      CPT2  ...  \\\n",
-      "0  -0.000000 -0.563521 -0.241056 -0.000000 -0.0000 -0.000000 -0.000000  ...   \n",
-      "2  -0.000000 -0.000000 -0.000000 -0.000000 -0.0000 -0.000000 -0.000000  ...   \n",
-      "3   0.401231 -0.000000  0.000000  0.390779 -0.0000  0.000000  0.397596  ...   \n",
-      "4  -0.064304 -0.000000 -0.000000 -0.047778 -0.4839 -0.000000 -0.022072  ...   \n",
-      "5   0.108820 -0.000000 -0.000000  0.112910 -0.0000  0.000000  0.112814  ...   \n",
-      "6   0.100690 -0.000000 -0.000000  0.063531 -0.0000  0.009537  0.064147  ...   \n",
-      "8  -0.000000  0.376553 -0.226248 -0.000000  0.0000 -0.000000 -0.000000  ...   \n",
-      "9  -0.744842  0.000000 -0.000000 -0.000000 -0.0000 -0.000000 -0.000000  ...   \n",
-      "10  0.000000 -0.000000  0.000000  0.000000  0.0000  0.000000  0.000000  ...   \n",
-      "\n",
-      "        PMEL    SLC1A5     VAMP2     TOP2A     TYK2      TYMS       VWF  \\\n",
-      "0  -0.000000 -0.013489 -0.588002  0.570760 -0.00000  0.535007 -0.000000   \n",
-      "2  -0.000000  0.042820 -0.000000  0.587580 -0.00000  0.477307 -0.000000   \n",
-      "3   0.000000  0.787002 -0.000000  0.727428 -0.00000  0.619025 -0.000000   \n",
-      "4  -0.136791  0.055904 -0.635303  0.615792 -0.00000  0.757284 -0.447168   \n",
-      "5   0.000000  0.389657 -0.493088  0.616645 -0.00000  0.737936 -0.000000   \n",
-      "6  -0.000000  0.309300 -0.522226  0.634868 -0.00000  0.647535 -0.000000   \n",
-      "8  -0.000000 -0.413306  0.000000 -0.722234  0.00000 -0.548652  0.000000   \n",
-      "9  -0.000000 -0.867376  0.000000 -0.604636 -0.00000  0.000000 -0.000000   \n",
-      "10  0.000000 -0.000000  0.000000 -0.299984  0.86833 -0.476531  0.000000   \n",
-      "\n",
-      "    CACNA2D2     RAMP1     SCN3B  \n",
-      "0  -0.000000 -1.000000 -0.534459  \n",
-      "2  -0.000000 -0.917447 -0.000000  \n",
-      "3   0.000000 -0.749312 -0.000000  \n",
-      "4  -0.465851 -1.000000 -0.000000  \n",
-      "5  -0.000000 -0.961777 -0.000000  \n",
-      "6  -0.000000 -1.000000 -0.000000  \n",
-      "8   0.000000  1.000000  0.000000  \n",
-      "9  -0.000000  0.638836  0.000000  \n",
-      "10  0.000000 -0.841079  0.000000  \n",
-      "\n",
-      "[9 rows x 39 columns]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Now filter those circuits that have stability above the desired threshold\n",
-    "stability_06 = stability[stability['stability'] > 0.6]\n",
-    "stability_06.head()\n",
-    "stability_06.reset_index(inplace=True)\n",
-    "\n",
-    "filtered_df.reset_index(inplace=True)\n",
-    "\n",
-    "filtered_df_stable = filtered_df[filtered_df['circuit_name'].isin(stability_06['circuit_name'])]\n",
-    "filtered_df_stable.to_csv(f\"{data_folder}/shap_filtered_above06_symbol.tsv\", sep='\\t', index=False)\n",
-    "\n",
-    "print(filtered_df_stable)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 153,
-   "id": "0c39a98d-eb37-41e0-9964-b9dc3efa134e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>drug_action</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>ACHE</th>\n",
-       "      <td>Inhibitor</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ADORA2A</th>\n",
-       "      <td>Inhibitor</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>AHR</th>\n",
-       "      <td>Activator</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ANXA2</th>\n",
-       "      <td>Activator</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ANXA4</th>\n",
-       "      <td>Activator</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        drug_action\n",
-       "ACHE      Inhibitor\n",
-       "ADORA2A   Inhibitor\n",
-       "AHR       Activator\n",
-       "ANXA2     Activator\n",
-       "ANXA4     Activator"
-      ]
-     },
-     "execution_count": 153,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Set back the circuit_name column as index\n",
-    "# filtered_df.set_index(\"circuit_name\", inplace=True)\n",
-    "\n",
-    "# Load annotations of the most common drug-effects over the drug-targets\n",
-    "annots =pd.read_csv(f\"/home/m3m/INFO_PROYECTO/drexml_paper_paquete/data/drugbank-v050110_mostdrugeffects_KDTs.tsv\", sep='\\t', index_col=0)\n",
-    "\n",
-    "## Subset the relevant drug-targets\n",
-    "annots = annots.loc[annots.index.intersection(filtered_df_stable.columns),]\n",
-    "\n",
-    "annots.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 156,
-   "id": "e18b7cb9-3ab5-422a-9a15-881768fc0ead",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": [
-       "<Figure size 2000x2000 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "## CREATE THE HEATMAP FUNCTION ###\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "drug_action_colors = {\n",
-    "    \"Inhibitor\": \"magenta\",\n",
-    "    \"Activator\": \"cyan\",\n",
-    "    \"Ligand\": \"yellow\",\n",
-    "    \"other\": \"grey\",\n",
-    "}\n",
-    "drug_action_colors_list = [drug_action_colors[action] for action in annots[\"drug_action\"]]\n",
-    "\n",
-    "# Separate the circuit names from the data\n",
-    "filtered_df_stable.set_index(\"circuit_name\", inplace=True)\n",
-    "circuit_names = filtered_df_stable.index\n",
-    "heatmap_data = filtered_df_stable\n",
-    "\n",
-    "sns.set(font_scale= 1.5) # Adjust the font size if necessary\n",
-    "\n",
-    "# Create the clustermap\n",
-    "cluster_map = sns.clustermap(heatmap_data, cmap=\"RdBu_r\", col_colors=[drug_action_colors_list],linewidths=0.5, cbar_kws={'label': 'SHAP Value'}, figsize=(20,20))\n",
-    "\n",
-    "# Add titles to the x and y axes\n",
-    "cluster_map.ax_heatmap.set_xlabel('Drug-target')\n",
-    "cluster_map.ax_heatmap.set_ylabel('Circuit name')\n",
-    "\n",
-    "\n",
-    "# Add a title to the color legend\n",
-    "ax = cluster_map.ax_col_colors\n",
-    "# ax.set_title(\"Drug Action\", pad=10)\n",
-    "legend_elements = [\n",
-    "    plt.Line2D([0], [0], marker='o', color='w', markerfacecolor=color, label=action, markersize=15)\n",
-    "    for action, color in drug_action_colors.items()\n",
-    "]\n",
-    "legend = plt.legend(handles=legend_elements, title=\"Drug Action\", loc='upper left', bbox_to_anchor=(15, 1))\n",
-    "legend.get_frame().set_facecolor('white')\n",
-    "\n",
-    "# Add a title to the entire figure\n",
-    "plt.suptitle('Heatmap of circuits and genes', fontsize=30, y=1.05)\n",
-    "\n",
-    "# Show the plot\n",
-    "plt.savefig(f\"{data_folder}/heatmap_circuits_KDT_drugeff.pdf\", dpi=300)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "id": "13cbc7c9-767b-457d-b034-a432082d3384",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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nztXChQtlMBj0wQcfaOzYsZmeiq5SpYpGjBihjz/+WN7e3vrpp580a9asApft9ttv19ixYyXZEss5TV1y6tQp/f777zIYDHrjjTdkMBj0559/6u+//872mMWLFys2NlYVKlTQpEmTHIlqSapevbqmTJmi0qVL69SpU1q9enW257Enye+66y61adNGUuH/uwAA4AyuaDs4U9myZTVu3DjHk1o5JbYBAHCHadOmSZLeeustPfjggxkS1ZJUq1Ytff755ypXrpwuXbqkOXPmOOW6M2fOdCSqP/vsM73xxhuZkuC33nqrZs2apaZNm8psNmvo0KGOWc8kac6cOUpLS1O9evU0duxYx4CzJNWrV0+TJ0+Wl5eXdu3apR07dmQ4t33a8oiIiAKV32q1OgbJn3zySdWsWVMpKSmO1/Jq/PjxOnXqlAICAgpUDgAA8qOgfWb7jJoLFizIMKtIfowePVphYWFKSUnRm2++qbS0tAzbP/zwQ+3evVshISGaOHGifHx8sj3Xtm3bdPz4cZUqVUo9e/aUJP3888+6ePFitscUpt0AFBUkqwE3u7bj17JlSz300EOSbHc/7969u9DnHzZsmEJDQ5WWlqbp06dn2JaYmKhJkyZJkl544YVcn3h66KGH1K1bN0m26dNOnDhR4HLdc889CgwMlCQdOnQo2/2WLFkiq9WqBg0aqG7duo4pR3Ma9P79998lSW3atMnyie3Q0FDdcccdkqQ9e/ZkeY4rV644EtnX/ndZv369zp07l9vbAwDAZVzddnAWX19f3XvvvZKkw4cPe7g0AIAbWUJCgqP/mtP01aGhofrXv/4lSfrrr78Kfd1z587p008/lWTrc+c0xae3t7dGjRolg8Gg8+fPa+XKlY5t9j7uQw89lCnJLtkS7fYpvvfu3Zthm/3J6oImq7dt26aTJ0/Kx8dH9957r6PdsWDBgjyfY+PGjVq4cKFuu+02R/0CAOAqhekzN2rUSLfddptjOvDExMR8X79s2bKaMGGCDAaD9u/f72gLSNKaNWv03//+V5ItqV2tWrUcz2VforJFixZq3LixqlSpIpPJlGGK8+sVpt0AFBUkqwE3++2333Tq1CkZjUa1a9dONWrUUMOGDSU55ymkgIAAdejQQZL0yy+/ZNi2bNkyJSQkyNvbW927d8/T+fr06SNfX1+ZTKZCPcFlMBhyXfv52obF/fffL0l69NFHJUkrVqzItrHQu3dvTZgwQU8++WSO55bkWO/jeqtWrVJSUpJKlSqlZs2aqWnTpgoLC5PZbHZMmQ4AgCe4uu3gTPYlQ5jyEwDgSd7e/6x6t27duhz37devn77//nt99NFHhb7ud999J5PJJC8vL7388su57h8eHq4PPvhAixYtyrD01fDhwzV27Fi1a9cu22PtsdZsNjteu3Llik6ePClJql27doHfg2S74bxUqVJ67LHHJElHjx7Vb7/9luvxCQkJGjFihHx9fTV+/PgM/y0AAHCFwvSZjUajxo8fL19fX505c0YffPBBgcrQvHlzx0NfX3zxhXbu3KmYmBgNHz5ckvTcc8/pgQceyPEciYmJ+vHHHyVJ7du3l8Fg0MMPPyzJdtNYduPaBW03AEUJyWrAzewdv7vvvlsVK1aU9E9CdtWqVYqPjy/0Ne68805JUkxMjKKiohyv26cyqVOnToapsnMSHBzseEpqw4YNBS7Thg0bdOXKFUm2acGzsnXrVp0+fVoGg8FRJw8++KB8fHyUlJSk5cuXZ3lcw4YN1bFjx2zvmI+NjdW2bdskKdOaIXb2/y7t27eXr6+vjEajozGwaNEipaen5+2NAgDgZO5oOzhDamqq1qxZI0mOGU0AAPCEwMBAxyxdkydP1ttvv63t27dnOUAbFham8PBwlS1bttDX3bp1qyTblJt5Xf+6Q4cOatiwYYakbtOmTfXkk09m2389fPiwjhw5IiljH/fgwYOyWq2qUKGCLl26pI8//livvvqqXn75Zb3//vvauXNnjmVJTEzUTz/9JOmftkatWrVUr149SXmbTvX9999XdHS0+vXrV+CEOQAA+VHYPnPt2rXVt29fSbak8ObNmwtUjsGDBysiIkIWi0UjR47UsGHDdOnSJdWtW1dDhw7N9Xj7w1SBgYGOJSrtN42dOnVKmzZtyvK4grYbgKKEZDXgRpcvX9bPP/8sSY6nnyVb8PT29lZqamq+14HKyrXTiVy7brV93ee6devm63z2jmlUVFSmNTdyY7FYtHHjRkdArlWrVrZ3kV27ZnSVKlUk2aZla9GihaSCr805ZswYJScnKyAgIMtrR0VF6Y8//pCU8b+L/fvz58/nejc+AACu4K62Q2GdOXNG/fr109mzZ2U0GtWrVy9PFwkAcIMbOXKkAgMDZbVatXTpUv3f//2f7r77bvXo0UMzZszQn3/+me0TSgV19OhRSfnvc+eH2WzWe++9J0mqWLGimjVr5thmnwI8ISFBjzzyiKZNm6aNGzfq119/1dy5c/Xcc89p2LBhMplMWZ77+++/d/Sd7bOdScowe9uFCxeyLdvPP/+sFStWqFGjRnmezQ0AgMJwVp/5lVdeUYMGDSRJI0aMKNB04L6+vvroo4/k5+enyMhIbdy4UYGBgZo0aZJ8fX1zPf7ah6kCAgIk2doU9qU9CjI2nlO7AShKmIsHcKOVK1cqJSVFfn5+GZKm5cqVU7NmzbRp0ybNnz9fL774YqGuExQU5Pg+Li7O8f2lS5ckSWXKlMnX+cqXLy/JFtzi4+MVFhaWYfvGjRvVpUuXTMclJSXp7NmzjrvX6tatqylTpsjHxyfTvgkJCVk2LOw/r1u3TgcPHtSuXbvy9bTW1KlTHWt/vfbaaypXrlymfewNgUqVKunuu+92vH7bbbepVq1aioyM1Pz58zN01gEAcAdntB3q1KnjlLJkFetNJpNiY2N1+vRpSbYn2d599101adLEKdcEAKCgbr31Vi1atEijRo1yPFGcmJioDRs2OGYNK1eunDp37qxevXo5BoULw973zetT1flltVr1zjvvaPv27ZKkt99+O8Pgtz1ZnZqaqmeeeUZdu3ZVjRo1dOHCBS1btkxTpkzRd999J19fX7377ruZzm/vG7dr1y7DuMKjjz6q//znP441M3v27Jnp2NjYWL3zzjvy8/PTuHHjslwzEwAAZ3PWeLuXl5fGjx+vTp066ezZsxo/frxGjx6d7/JERESoW7dumjlzpiTp6aef1s0335zrcUePHtWuXbskZR4bf+yxx/TRRx9pw4YNOnfunCpVqpSnsuTWbgCKEp6sBtxo8eLFkqS2bdsqODg4wzZ7EIqKisrTOlA5ye4uaftT0Vkli3NybSczqzUoL168qD/++CPT14EDB2QwGPT444/r008/1eLFizM89X2tlStXKjU1Vb6+vnrwwQczbGvXrp2jvvJzB9lnn32mTz75RJKtzl999dVM+5jNZi1btkySrQNuX2vTzj7Vyq+//qoTJ07k+doAADiDM9oOjRs3zvXr+nNnJatYv2fPHp0+fVotWrTQm2++qZ9//lmPP/54Id4xAADOEx4erm+++UZLly5V3759dccdd2ToD1+8eFHTpk1Thw4ddO7cuUJfz57wdsUyUmazWSNGjNCiRYskSV27dtUjjzySYZ+77rpLTz/9tEaNGqX33ntP4eHh8vX1VZUqVdS7d2+NHTtWkq1fffjw4QzHRkZG6s8//5SUeZC8QoUKuueeeyRlv2bmO++8o4sXL2rAgAGqVauWc940AAC5cOZ4e3h4uPr16yfJtizkxo0b812eixcvaunSpY6fFy5cqOPHj+d6nP2GsbCwMEfMtevQoYOMRqPMZrMWLlyYp3Lkpd0AFCUkqwE3OXLkiP766y9JmTt+knT//fcrMDBQkjRv3rxCXevy5cuO70uXLu343v5EdX7XtrQ/kW00GrN8KrtTp046ePCgDh48qAMHDuj333/XkCFD5O/vr/j4ePn6+qpNmzY53lltD8itW7dWSEhIhm1+fn5q3769JNvaHdc+LZ6V9PR0jRo1SpMnT5YktWjRQpMmTZLBYMi076ZNm3T+/HlJWf936dChgwwGg6xWa4GnIQcAoCCc1XaYN29erl+33nprruWxx/qDBw9qz549mj17tmOpkOjoaN13332O2VgAAChK6tWrp379+mn+/Pnavn27Zs+erZdfftkx89aJEyc0YMCAQl/HPguZvQ/tLElJSerbt6++/fZbSdITTzyhYcOGZdqvQ4cO+ve//63nnnsuy/N06NBBNWvWlNVq1Zo1azJss5+7XLlyat68eaZj7TejnT59OtOamcuWLdNPP/2kO+64Qy+99FL+3yAAAAXgivH27t27O6YDHzlyZIZx9txYrVa99dZbiomJUXh4uMqWLaukpCQNHjw4xxvZrn+Y6vox9EqVKjlmL1u0aFGuN8Xltd0AFCVMAw64iT04SFLv3r1z3HfNmjW6cOFCgQd8IyMjHd9fe0dz3bp1tXnzZsfUYHllX+u6Zs2auU4VYjAYVKZMGb300kuqU6eOXn31VS1atEgXL17UZ599lmXC+tChQ9q7d68k6aeffspxulL7OiPZdYATExPVv39//frrr5Kkhx9+WBMmTMi23PYkuZR1o+Zaixcv1uuvv850KQAAt3Bn2yG/fH191bx5czVq1EhdunTRoUOH9MILL2ju3Lk8TQUAKNICAgLUvHlzNW/eXAMGDNCwYcP0/fffa/fu3dq3b5/q169f4HPffPPNOnr0qA4dOpTnY06ePKlSpUplu1xXdHS0evXqpf3790uSXnzxRQ0ZMiTLm7Hzol69ejp27JhOnTrleC09PV3Lly+XZHsiLLc6mDdvnlq1auUo35gxY+Tv769x48Zlmq0MAABXcUWf+drpwM+dO6dx48Y5ZibJzcyZM7V582YZjUb9+9//1tmzZ/XGG2/or7/+0uTJkzVw4MAsj9uwYYNiYmIkSV9++aW+/PLLbK9x/vx5rV271vFg1/W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t4qOUnBCt5AT3SOTEWFvIB3JNJvc047HRVqWlHLwh2zAMlZTbVVhapf0lVdpfWq2SsmpVVju1o6BMOwrKJB0MXqclxyo1JbbBwHg4CGqw+qmnntLzzz8vl8ulCRMm1DklaG5urrZu3apt27Zp4cKFuu6663TNNdcEobQAAISfYLe1r776qqZPny6n06mLL75Y99xzT6PeZzab1bFjx3r3jxkzRq+++qp+/fXXFpXP4WhZIMkT8HC53MFvuLkOLALkdLpaXMe+yAN1o279i/r1H+q2cYLdBwAAAI1Huw0Abk6Xob1Fldq9v0K7CyvcU2QfwmI2KSUx2j3KOClGyYlRsphbzw3JJpNJSfFRSoqPUpd091IPDqdL+0uqtLeoUnuKK7W/pKpW8Do5IUrpKXFKS4lVckJUyAfrfy9owernnntOzz77rAzDUEZGRr3p4uLidPPNN+udd97Rjh079NRTTyk6OlqXX355AEsLAED4CWZb63A4dP/992v27NkymUy6+eabdfXVVzc7v9/r0KGDJKmiosJneQIA0FpwvQ0AQPig3QYQ6ewOl3bvr9CufWXavb9CDufBASEmSSmJ0UpvG6tO6UmKtbWewHRjWS1mpSbHKjXZPQLbE7wuKKzQ7v0VKi63u9ftLq3WbzmFiomyKKNdnDLaxaldUkxYBK6D8q+6detWzZgxQ5I0ZcoUffHFF3XeLSZJKSkpmjx5sj777DNdcsklMgxDTz75pHJycgJZZAAAwkow29rKykpNnjxZs2fPVmxsrJ588skmB6pnzpypG264QUuWLKlzf15enqSDQWsAAODG9TYAAOGDdhtApKqsdmpbXomWrs3XvOXb9cOGAu3cUy6H01BMlEVd0hM0pE+qTh3WWScOyFCfLilqnxwrszn0A6/+5gle9+vWViMHZerkIZ00sFc7ZbSLk9ViUmW1U1t2lWjx6nzNW5Gjnzft0e79FSE9K2RQgtVvvfWWHA6HLrroIl1//fWy2WyHfY/NZtPUqVN1xhlnyG63a/bs2QEoKQAA4SlYba3T6dSUKVO0aNEitWvXTrNmzdKpp57a5Hy2bNmiefPm6YMPPqhzv2f7yJEjm5w3AACtGdfbAACED9ptAJHE7nBpe36JFq/O0xcrcvRz9l53ENWQ4mOs6pWZpD8MyNDJQzrp6F7t1bF9vGzW8F6LORBio63qmp6ooUekaeyxXXRs3zR1TkuQzWpWtd2lbfml7psCDgSu9xZVyjBCK3AdlGD1kiVLZLVam7Wmxs033yzDMLR48WI/lAwAgNYhWG3ts88+q0WLFikuLk7/+9//NGDAgAbT2+12ZWdnKzs7W3b7wTVoLrroIplMJn300Uf66KOParxn1qxZmjNnjpKTkzVp0qQmlxEAgNaM620AAMIH7TaA1s7pcmnn3jKtWL9b85Zv10+b9mpPUaUkqU18lI7okqxRgzpq9OBM9evWVimJ0WExbXWosphN6tA2ToN6t9fYoZ113JHp6pqeoCibWXaHO3D9/eo8zf8hV+u37a+1JniwBGXN6pycHKWnp6t9+/ZNfm9mZqa6du2q7du3+6FkAIBwZxiGqh0ulVc6VFHtkN3uUrXDJbvDeeDRJZfLkMswZLWYtTW/RJPG9gl2sX0uGG1tUVGRXn75ZUlSWlqann/++XrTnnnmmRoxYoTy8/N12mmnSZLmz5+vTp06SZIGDRqkW265RY899pj++te/6uWXX1bXrl21ceNGbd68WXFxcXrmmWfUrl27Jn8+AABaM66361ZcVq03PlqjartLe4sq5HC6ZDabZDGZZLWaFW2zKDrKohib+3lMlFVRNjNflAEA/Ip2G0BrZBiG9hVXKWd3qXbuLauxBnVirE2ZqfHKTI1XfMzhZ5NA85nNJqUlxyotOVYDDEN7iiq1o6BMO/eUqbzKoQ07irRhR5FSEqPVt2uKznC6glbWoASrq6ur1aZNm2a/PykpSbm5uT4sEQAg3BiGodIKu4rKqlVcVq3SCrvKKh0qr3TI2YT1N3btLdcZw7uqTVyUH0sbeMFoa5cvX67y8nJJ7nW3tm7dWm/avn37asSIEQ3md9VVV6l///565ZVX9PPPPys7O1upqak6//zzdfXVVyszM7NJ5QMAIBJwvV23bfklWr42v0nvsVpMSoi1KT7GpoRYmxJirWqTEK34GCtBbACAT9BuA2hNKqoc2lFQqu35pSqrdHi3x0RZlJkar06pCUqKs9GXDgKTyaTU5FilJseqf4+2yttXrh27y7S7sEL7S6q0eHWePvwmW6cM6RSU8gUlWJ2cnKydO3c2+/27du1SfHy8D0sEAAh1FVUO7S2u1L7iKhWWVqmk3N4FQ5lTAAEAAElEQVRgUDomyqLYaKuibWbZrBbZrGZFWc2yWc2ymE0ymUxqkxCt8SN6Ka1NtKqqHPXmFY6C0daefPLJ+u2335r0nk6dOjX4nuHDh2v48OFNyhMAgEjG9XbdjureVndfMkQVDkPLf92pwtIquVyGnC5DdodLVXbnwZ9q92uH01BhabUKS6tr5GW1mJScEK3khCglJ0SrbVK0YqKC8vUKACDM0W4DCHcul6G8/eXanl+q3fsrvNstZpMy28erU1qC2iUxtXcosVrM6pSaoE6pCaqsdii3oExllQ4NykoNXpmCcdA+ffpo8eLFys7OVs+ePZv03k2bNmnPnj2HXQMTABDeKqocKiis0J4id4C6vI5gssVsUlJ8lNrERykxzqb4GKviom2KjbHIYjYf9hipKXHq1TlZxcUVh00bbmhrAQCITPQB6mYymdS3W1slJcWqsKhCBfvLG0zvdLlUVulQWYVdpRXux5LyahWV2+VwuqfQ86y1J0kJsTa1bxOj9skxap8Uoyibxd8fCQDQCtBuAwhXu/aUacW6fG3YXqhqx8Hpo9smRatLWoI6to+X1XL472cRXDFRVvXMbKPUlDj17BS878mDEqweNWqUvv/+e82cOVOPPfZYk9777LPPymQy6dhjj/VT6QAAwWAYhvaXVCl/f4Xy95eruMxeK02b+Ci1axOjlMRotYmPYgrGBtDWAgAQmegD+IbFbFZSXJSSfrdUjMtlqKS8+sCI6yrtLz24JE1phV1b80okSckJUerQNk4d2sYpkakOAQD1oN0GEE5cLkM/Z+/R16t26tfNe73bo20WdU5LUJf0BCXEsg41mi4oweqzzjpLM2bM0Keffqru3bvruuuua9T7nnvuOX3yySeyWq06//zz/VxKAIC/uVzuUSm5e8qUv6+8xl14kpSSEKX2ybFql+QOUNus3I3XWLS1AABEJvoA/mU2u5eSaZMQra5KlCRV253aW+weab2nsFIlFXbv9OHrtxcqLtrqDly3c/drCVwDADxotwGEg6LSKn3780598/NO7Suu8m7PTI1Xx3bxSm8bKzN9XLRAUILVCQkJmj59uq655ho988wzWrRoka666ioNGzas1hobpaWlWrp0qV5++WX99NNPMplMuu2229SlS5dgFB0A0EIuw9Deokrt3FOmXXtrBqhtFrNSU2KUnhKntJRYRTN9YrPR1gIAEJnoAwRelM2ijHbxymjnrt/Kaofy91Uob1+5CgorVF7l0OZdxdq8q1gxURZlto9XZmq82sRHEbgGgAhHuw0gVBmGod+2F2rhqlz9uKFATpchyb38zUlHd9RZI3tryS+52lvY+pZXROAFJVgtuac4uf322/XYY4/p559/1pQpU2SxWJSZmamUlBQ5HA7t379f+fn5cjqdMgxDJpNJkydP1qWXXhqsYgMAmqmkvFo5u0uVs7tMVXand3uUzayO7eLVsX2c2ibFcBeeD9HWAkD4MptNMptpEw9lYb2zRqMPEFwxUVZ17ZCorh0S5XC6VFDoDlzn7a1QZbVT2TuLlb2zWAmxVmWmJqhzaoLiYoL29QwAIMhotwGEErvDqaVr8vXlyh3aUVDq3d4zM0mjBmVq6BFpio2xKSUlvoFcgKYJ6tXQ5ZdfroEDB+qee+7R5s2b5XA4tG3bNm3btq1W2oEDB+quu+7SwIEDg1BSAEBz2B0u7dxTpu27S7W/5OAUMVFWszLaxalj+3i1a0OA2p9oawEg/JjNJqWkxBOsroPny1kcHn2A0GC1mL2jrp09De3eX67cgjLl7a9QaYVDv20v1G/bC5WaHKOu6Ynq0DaO330AiEC02wCCbX9JlRauytU3P+WqpNwuyT3IaPiRHTRqUKa6pCcGuYRozYJ+6+4xxxyjTz/9VCtXrtSSJUu0efNmFRUVKTY2VqmpqerevbvGjBmjTp06BbuoAIBGKi6v1tZdJcrZXeqdIsYkKS0lVl3SE5SewpdwgURbCwDhxTOqesGKbSo85GavSNc2KUYjh3ShD9EE9AFCi8Vs8gau7Q6Xdu0r147dpdpTVKmCQvdPlM2sLmkJ6pqeqPhYW7CLDAAIINptAMGwZVexvlyZoxXrdnu/x22bFK0xx3TSSQM7Kj6GPin8L+jBao8hQ4ZoyJAhwS4GAKCZXIah/H3l2rKrRHuKKr3bE2Kt6pyWqM5p8YqJCplmJyLR1gJAeCksqWL9r0MwE0vz0QcIPTarOyjdJS1BpRV2bc8vVc7uElXZXdqUW6xNucVKT4lVj45Jat8mhhkFACCC0G4D8Deny6UfN+zRlytztGlHkXd7r05tdMqQzhqU1V4WM8swIXCIGgAAWqTK7tT2/BJtzStRRdXBtagz2sWpe4dEtePLNbRiLsNQcVm1ikqrtK+4UtUOl6odLtntTlU7XHI4XXK6DPeP05DLZcjpcm8zjPrzNZtNshz4OfS5xWKW1WJSlNWiKKtZNpu5xnObxczvGwAAYSYh1qZ+3VJ0RJdk5e8v19a8EhUUVip/f4Xy91coMc6mHhlJ6pQaz9rtAAAAaLbSCru++3mn5v+4Q/uK3bN4WcwmHds3TX8c0lndM5KCXEJEKoLVAIBmKa2wK3tnsXJ2l8p1YIqYKKtZXTskqmuHRMVF08Sgdau2O3XXC0uVt6882EXxMpukmCirYqIs7p9o9/PYKItio62Ki7Ep2kZAGwCAUGQ+ZJrwkvJqbTmwrE5JuV0/Z+/Vum371bVDorp3SFQMfW0AAAA00s49Zfrqh/9n777jo6ryPo5/p6U3EiABQofQEelgoakI6uKCdRXUde1tfdxVEBZXFFB3xV1F1rarIqtgd1ERlWKjI4jUEGqAJIRU0jMz9/ljMkMCSUjIpEzyeb9eyUxuOXPuvZN77rm/e845ojXbk1RU7JQkhQbZNLJfG43q30YRIf71nEM0ddRuAADVknmyUAlHs3Qs7VSALiLETx1bhal18yC6iEGT4TQMFdtdF/g2q6tVs81qlp+7tbPNLKvFXG7raIvZpArjxYbkNCSns2yrbEdJq+xih+FpuV1kd6rY7lBRsWtZpyHlFdqVV2ivMN8Ws0nBAa7AdXCA1fUTaFNokE3+NguBbAAAGoDQID/17RylHu0jdCglRweTTiqv0K69R7K072iWYluGqEubcIUwrjUAAADKYRiGth9I1zebErV9f7pnetuWIbp0YFsN6dlSNqulHnMInEKwGgBwVoZhKDWzQAlHs8qMR92yWaC6tglXZJg/AS40OQF+Vv3joYsUHBKgL37Yp9SM+m1h7XAaKix2qKDQroIihwqKHMr3vHcFsPMLHXI4DWXnFSs7r/iMNGxWs0JLAtchQTaFBvopLNjGePMAANQTm9WiLm3C1bl1mJLT87TvWLbSswt1OCVHh1Ny1DoqSF1jwxVOaxgAAADINWTj2u3J+mZTopJKGhuZJPXr2lyXDWqruLYR3MdFg8OdRwBAhQzDUHJ6nvYkZik7t0iSZDJJbZoHq0ubcIUF+9VzDoH6ZTabZLM2jN4ELGaTgvytlXbB73Aayi+wK7egWLkFduWVvObku16L7U6lnyxU+snCMuv528wKD/ZXeIifwoNdP0EBVio3AADUEZPpVBfhadkFSjiSpZSMfB1Ly9OxtDy1iAhQ19hwRYUFUD4DAAA0QRknC7Xy5yNaveWocgtcPe4F+Fl0Ud/WGjMwVi0jAus5h0DFCFYDAM5gGIaOZ+Rr9+FMZZUEqS1mk9pHh6pTmzDGowZ8lMVsUkhJq+nTORxO5RTYdTKvSDl5xTqZX+x6n29XYbFTxzPzdTwz37O81WLyBK4jQvzVLNSfADYAAHUgKixAUT0DlJ1bpL1Hs3QsNVepmQVKzSxQs1B/dWsXoRbhBK0BAACaggNJ2fp6Y6I27T4uh9OQJLWICNAlA9vqwj6tFMh9XPgAvqUAAA/DMJSaVaA9hzOVUdKy0mI2qVPrMHVuHSY/G+OYAI2VxWL2BJ9Lszucys4rUlZOkbJzi5SV63q1OwylZRcqLbtQ0klJkp/VrGah/mrdIkQd20SoZZi//BpIy3MAABqbsGA/DYhroe7tIrTvaLYOH89RxslCrduRQtAaAACgEXM4nfo5/oS+2ZiohKNZnund2kboskFtdV6X5jKbuQaE7yBYDQCQJJ3IKtDuwxlKzz4VpO4QE6ouseHyJ0gNNFlWi1mRoQGKDA3wTHM6DeXkFyszt0hZOYXKzHG9FtmdSsnIV0pGvrbEp8okKSYqSJ1bh6tLbLji2kYoulkgN80BAPCi4ACb+naOUlzbcCUcydbBlJMErQEAABqhvIJiff9LklZsTixpPOC6hzukZ7QuHdhW7WNC6zmHwLkhWA0ATVx6doF2H87UiawCSZLZJLWPCVXX2HAF+FFMADiT2WxSWLCfa9z6liGSXONhZ+cWKeNkofIK7cortCs5LU9JJT8//pokydUKrGtsuOJiIxTXNkKxLYNlMdP6GgCAmgrws6p3p0h1iQ0jaA0AANCIpKTn6dtNR/Tjr0kqLHZIkkICbRp1fhuN7t9G4SH+9ZxDoGaIQgBAE5VxslB7Dmd6xqA1maT20a4gNWOZAKgui9mkZqGusaujIgI1cXScDh3JUHxipvYdzdLexEztTzqp7Nwibd6Tqs17UiVJAX4WdW4TrriSltcdWzHkAAAANUHQGgAAwPcZhqFdhzL07aYj+iXhhIyS6W1aBOvSgW01rFe0bFbun6BxIBoBAE1MVk6hdh/OVEpGSZBaUtvoEMXFRigogGIBgPeEBfupX5fm6teluSSp2O7QgaST2nskU/GJWUo4mqn8Qod2HEjXjgPpkiSrxaTOrcPVs2OkBvdupZZh/uJWOgAA1VdZ0DoyzF/d2zVTVERgfWcTAAAApeQX2rVme7JW/nxESWl5nul9O0fpskFt1aN9Mx46RKNDVAIAmohDSdlaufmIDiWf9EyLbRGsbm0jFBxoq8ecAWgqbFaL4tq6uv++Yphr7OsjqTnaeyRL8YmZij+SqaycIu1JzNSexEx98v1+2axmdWkTru7tItS9fTN1bBUmq4VuwwEAqKqyQessHUw+qfTsQq3ZnqwDSdnq0bG5YiLoOhIAAKA+HU3N0cqfj2rNjmQVFrm6+vb3s2h47xhdOrCtYiKD6jmHQO0hWA0AjVxSWq6W/nRQ63elyCjpL6ZNc1eQOiSIIDWA+mM2m9QuOlTtokM1ZkCsDMNQSka+dh/KcAWsD2cqM6dQuw5laNehDOmHA/KzmdU1NsITvO4QE8qY1wAAVIEraB2lzm3CtfdIlg6lnFRSWp4enf+D+naO0oQLO6pjq7D6ziYAAECT4XA6tSX+hFb+fES7D2d6preKCtLo/rEa3juG4RrRJPAtB4BGKiUjT0t/Oqi1O5I9Qer2MaHqGBOqsGC/+s0cAJTDZDIpJjJIMZFBumRQW0VEBGnH3uPavj9duw9naM/hTOXkF5fpNjzAz9Vau3u7ZurePkLtWobKbKY7LAAAKhLob1XfzlHq0iZch4/nKOFolrbtS9O2fWnq16W5rr6oo9pFh9Z3NgEAABqtrJxCfffLMX239ZgyThZKkkwmqX/XFhrdv42609U3mhiC1QDQyJzIytfSnw7qp1+T5SyJUp8f11y3XtlbW/ekKC0zv55zCABVYzKZ1KZFiKKbBWnMgFg5DUNHU3O1+1CGJ3idV2j33GCXpCB/qyt43b6ZureLUGzLEJmp4AEAcIagAKsu6NtKf7p5oN7+fIfWbE/S1oQT2ppwQgO6tdDVF3ZUe1paAwAAeIXTMLTzQLq+/+WYtuw9IYfTdd82LMimi/u11sh+bRQZFlDPuQTqB8FqAGgk0rML9PnaQ/rhl2Oei50+naJ09UUd1bVthJo1C9bWPSn1nEsAOHdmk0ltW4aobcsQXTqorZxOQ4nHc7T7cIZ2H8pQ/BFX8Np9o12SQgJt6uYOXrdvptZRQTydDABAKa2aB+uuCb00fmg7/e+ng9qwM0Wb96Tq5z2pGtIrWrdc2UvBNobcAAAAOBfp2QX6cVuSftiWpLTsAs/0zm3CNKZ/rAZ0aymblWstNG0EqwHAx2XmFOqLtYf03dajsjtcQeoe7Zvptxd1UpfY8HrOHQDUHrPZpPYxoWofE6qxg9vJ4XTqcEqOdpeMcb33SJZy8ou1OT5Vm+NTJUlhwX6u8a7buYLX0c0CCV4DACCpVVSw7vpNL10xrL0++/GANu9J1bodKdqwM0XDerfSVcPbq2WzoPrOJgAAQINndzi1eU+qfth2TL/uT/MM0Rjkb9Ww3jG6+LzWatsypH4zCTQgBKsBwEdl5xVp2bpDWvXzURXZnZKkuNhw/fbiTurWrlk95w4A6p7FbFbHVmHq2CpM44a2l93h1MGkk9pV0vI64WiWsnOLtGHXcW3YdVySFBHiV9JluCt43SI8gOA1AKBJi20Rovt+20eHU07qs58OaEv8Cf30a5LWbk/WhX1jdOXwDmoeHljf2QQAAGhwjqTm6LOfDurbDYeVlVvkmd69XYQuOq+1BsS1kJ/NUo85BBomgtUA4GNy8ou1fMNhfbvpiAqLHZKkzq3DdPXFndSzfTOCLABQwmoxq0tsuLrEhuuq4R1UbHdq/7Es7T6cqd2HMrTvWJYyc4q0bkeK1u1wDZMQFebvCV73aN+M8aIAAE1Wu+hQPXxdP6WeLNLbn+/Qtn1p+v6XJP30a7IuPq+1rhzeQc1C/es7mwAAAPUqK6dQ63emaM2OZB1OyfFMDwv20wV9YnRx39aKjqR3GqAyBKsBwEfkFRTr642J+npjogqKXEHqDjGhuvqiTurTKZIgNQCchc1qVrd2zdStXTNNuLCjiood2nc0S7sOZ2r34QwdOJattOxC/fRrsn76NVmS1DIiUN3bu7oNj2sbQfAaANDkxLVrpj/deL52H8zQJz/s165DGVq15ah+2Jakkee31hVD2ys8hKA1AABoOgqLHdoSn6o1O5K140C6p5tvi9mkgT2iNaRHS/XuGCmrhbGogaogWA0ADZw7SP3NpiPKL7RLcnXN99uLOqpf1+YEqQHgHPnZLOrRIVI9OkRKkgqK7Eo4klXSbXimDiZn63hmvo5n5uv7X5IkSc3DA9Q1Nlxd20aoa2yEWkUFycx5GADQBHSJDdefbzxfew5n6JPv9yv+SJa+3XRE3289ptEDYnX5kHYKC/Kr72wCAADUiqJih37dn65Ne45r694Tnh4vJVevl8N6x2hY7xi1a9NMGRm5spcM2wjg7AhWA0ADlVdg1zebXC2p3UHqNi2CNeGCjurfrQXBEQDwsgA/q3p3ilLvTlGSpPxCu+ITXa2udx/O1OGUkzqRVaATWQVaW9JteEigTV3ahKtr23B1jY1Qh5hQnpwGADRq3do102M39dfOkpbW+49l66v1h7Xq56O6ZGCsxg5up5BAW31nEwAAoMaK7Q5t35+ujbuPa0vCCRUWnQpQNw8P0LBergB1TEk331Yr9wOAc0GwGgAamLwCu74tCVLnuYPUzYP1mws7agBBagCoM4H+Vp3XpbnO69Jckit4vf9YtuITM7X3SKb2H8tWTn6xtiac0NaEE5JcXY13ahWmrm3D1aVNhDq1DuOGPQCg0TGZTOrVMVI9OzTTr/vT9MkPB3Qo+aS+WHtIK38+oksHttVlg9opKIDbTgAAwLfkFdi1/UCatu511fULSgWoI8P8NbBbSw3q0VKdWoXR4yXgJdQaAKCByC8saUm94VSQunXzYP3mgg4a2L0lQWoAqGeB/lb16hipXh1d3YbbHU4dSjmpvYlZ2nskU3uPZCknv1h7EjO1JzFT0iFJUstmgerUOkydW4erU+swtW0ZQutrAECjYDKZ1Ldzc/XpFKUte0/o0x8O6Ehqjv7300Gt2HxEYwe30yUDYxXgx+0nAADQcKVm5rseRN97QvGJmXI4Dc+8ZqH+GtS9pQZ1b6mOrcO4RwvUAmoLAFDP8grsWr7+kL7emKjcAleQulVUkCZc2JEgNQA0YFaLWZ1bh6tz63BdPqSdDMNQcnqe9h7JUnxipvYdy1ZKep6OZ+TreEa+1pV0HW61mNU+OkSdSoLXnVqHqXl4AE9kAwB8lslkUv+4FurXtbk270nVpz/sV1Janj7+fr++3pio8UPba1T/NvK3Weo7qwAAACq2O5VwNEs7DqTrl30ndDQ1t8z8VlFBOq9Lc/Xv2kKd2hCgBmobwWoAqCdZOYX635pD+uKn/covdHUn4wlSd2sps5mLIADwJSaTSa2igtUqKlgXn9dakpSTX6wDSdnaf6zkJylLufl27TuWrX3Hsj3rBgdY1T4mVO1jwtQ+JlQdYkIVExlUK2WBpaRVt6WBtO52Og05Sz21DgDwXWaTSYO6t9SAuBZavytF//vxgFIy8vX+qgR9teGwxg9trxHntZa/H0FrAABQdwzD0JHUXO04kK6dB9MVn5ipIrvTM99sMimubbjO69Jc/bo0V3TJGNQA6gbBagCoYycy87Vsw2H9uC1JxSUXRa2bB+uq4R00qDtBagBoTEICberTKUp9OkXJbDYpIiJIKel52nM4Q/GHMrTncIYOHMtSboFdOw9maOfBDM+6/n4WV+vrNuHq3Mb12i4mTDard4LMYWGBXkmnppxOQxkZuQSsAaARMZtNGtYrRoN7tNTa7Sn6308HdCKrQItX7NXnaw5qzIBYjRkQq5BAW31nFQAANEJOw9Cx1FzPkF07D2UoO7eozDLhwX7q2aGZepfU2bkuAeoPwWoAqCNHU3P05bpDWr/zuJyG64Z8t3bNdPmQtq4gBt3JAECjZjabZLGYtftgmjJPFqpFRIBaRLTSsF7RysgpVHpWodKyC5SWVaD07AIVFjm062C6dh1M96RhMklhwX5qFuKviFDXT7MQf4UF+1X5YSezySR/f6sKC+2e8qi+RIT6a/Sg9jKbTQSrAaARspjNurBvKw3tFa2ffk3SsnWHdTwzX5/9eEDL1h/Sxee11thB7RQVHlDfWQUAAD6sqNihA0nZ2nskSwlHs5RwJEt5hfYyy/jZzOrWtpl6dWimnh0j1aZ5MMNxAQ0EwWoAqGX7jmbpi7WHtDXhhGdarw7NdNWFHTW8X6wyM/NkL9XtDACgccs8Wai0zPwy00ySosL8FRXmL8WGyzAM5eQXKyunSFm5JT85RSp2OF3Tcoqk5JOn1je5WnGHBtkUGuSn0CCbQgJsCg6wntHdt9lsUmCgn/LziwgQAwDqhNVi1oh+bXRR39batOe4lq07rEMpJ/XtpiNa9fNRDe4RrXFD2ym2RUh9ZxUAADRwhcUOJR7P0aHkk66flJM6diJXjtPqt/42izq3CVPX2Ah1axuhzm3CvdZTGQDvIlgNALXA4XRqS/wJfb0xUQlHsyS5AhH9u7XQ+KHt1bFVmKxWM0/vAQDKZTKZSoLOfootmWYYhgqKHDqZV6TsvGKdzCvWybwincwrlsNplPxdLCmvTFqB/hYFB9gUHGhTSIBVIUF+at7MEKOFAgDqmtls0uAe0RrUvaV2HszQl+sOadehDK3dkay1O5LVt3OUxg1pp7i2EdSVAKCJO5aaoxOZ+Uo/WSDD6aoPOQ33qyHDkAxDksl1z00q/d7kei01z2Q2yWIyyWwu+TGV9H5VZppJJpMogxoIh9Op1MwCJZ3I1bG0XB07kavDKTk6lpar8joJCw/xU9fYCHWNDVdcbIRiWwbLYiY4DfgCgtUA4EV5BXb9sO2Yvt10RGnZBZIkS8l4beOGtlOrqOB6ziEAwFeZTCYF+lsV6G9Vy2anphuGofxChydwnZ1XpJz8YuXkF8vucM3LL3ToRFbBGWkG+FkU5G9VYIBVQf7WMu8D/S1U7AEAtcJkMqlXx0j16hipA0nZWrbukDbvSdW2fWnati9N7aJDdMmAthrSs6VsVh6vAoCmZtnaQ1r09Z56+WyTJIvFJKvFLKvFJIu55NViltXsml7efNffJe+tpd5bzAz9VwmH06mM7EKdyCoo+clXUlqejqXlKiU9T3ZH+b2BhQX7qUNMqNpHh7peY0LVLNSfBw0AH0WwGgC84Hhmvr7dlKgftiWpsMghydUd68jz22h0/zaKCPGv5xwCABork8mkoACrggKsio48Nd0wDBXZncrNL1ZOgV25+cWunwK7cgtcgeyCIocKihzSycJy0/azmRXgZ1WAn0UBNosC/Czy97OcmuZnkb/NUuXxsgEAOF3HVmG697d9lJKep+UbDmvN9mQdTsnRf77cpQ9WJ2hkvzYaRZ0KAJqUVs2DFB0ZpJy8IhmGa9gjs9nV6tnV+tnVMtoVWnYFM42SX57QpmF43huG5DQMOZ1Gyeupv0/vOtqQZHcYsjscXtsei9kki8Ukm6VsENtqMSs4yKbM3GKZnE75Wc0K8HfXtVwPEJeuewX6W2W1+MYDxe6HqrNyC5Vdamgr12uh0rJdwen07EI5y2smXcLPalZMVJBaNw9Wq6hgtW0R4glMA2g8CFY3QO5uRxor97iJp4+feDZOp8G4imhQnE5D2/anafWWo/p1X5rnArh182BdOjBWw3rFyM9GKwAAQP0wmUzyt7mCyZFhp6abzSYFBNiUmV2g3Pxi5RXalV9gd70Wul7zCuxyOA0VFTtVVFyk7NzKP8vPZpa/zSI/q1l+p73abBb5W82y2czys7qmWS3mRn29CwCovujIIE25vLsmjuis7385ppU/H1F6dqGWrjmoL9cd0qAeLTVmQKw6tQqj1RQanLq+l3eu99bqGvfycK76dW2hiwe006er9io1I+/sK9SQJ5BdErx2OA3ZHU45HIbszpJXh1N2hyGHs+S15G+7wym70z2/ZJrd9d799XenWVTsLPfz4w9nVjmvVoupVADbqgB/iwJPC2i75/nbzLJYzLKUnKMsZldLcD8/syLCcpWfV+gK1huGnJIr2F/S3bp0qtt1u8NQUbFDxXaniu1OFdkdJa9OFZSqQ5Z9La6wVXR52xQVFqDm4QGKCg9UTGSQWjcPUquoYEWFB9AyHWgCCFY3MGazSc2aBTeJm3dhYYHVWt7pNJSRkctFLupdVk6hvt+WpO+3HlVa9qmWaL07RuqyQW3Vq2MkN08AAA2ayWRSgJ8rcFzeE+nuVtkFhXYVFDs8LbALixwqKLKX+duQSoLa5d94qYjFbJLNatZX6w/L32ou23qg5AaLv9Uim80sm8UV/LZZzLJZXT/uQLhrnmsZq9XVxZ77RkzpV1+5weEeg8/hNMrc4PX8LtNapNR7GZ7WJKVeSi1z2rpSqeWNCtJ0tVwxm1xj/LnH8DObXWMAcr0DoDaEBNo0fmh7jR3cVj/Hn9A3mxKVcCRL63akaN2OFLVtGaKR/VprSM8YBQVwWwv1rz7v5VX33lpd414efIXZZJLZYpK83ObEWSqIXexwB8BPvbc7nPKzWdShdYQys/OVV2B31cGKHMovVe8qKLSryO6qb9kdhmfYJ18Q6G9VeLCfwoP9FFbyGh7ip8iS4HTz8ECFh/j5TH0NQO3gqr6BcT+JuXLjIWVW0B2jrzObTPL3t6qw0F5pFx+lRYT6a/Sg9jKbTVzgol44DUN7Dmdq9Zaj+jk+1dNFUHCAVRf0aaWR57dRTGRQPecSAADvKN0qO7yS5QzDUGGxU4VFrpsnRcWup+wre3WXoQ6nIUeRQ0knztJs21vbJJ0RxK5KsPVsixglrQ3c790f5nQapboiPBUoNtzBYaNk3XKCyL7C3Q2j2WSSyaxTwWyTydOCo8xPyZh+ZrNJVvdxKGecP5t7TMCS9+YqHisAjYvFbNag7i01qHtLHUzO1rebjmjDruNKPJ6jd76O15JVCRrcI1oj+7VRx1ahnCdQb+rjXt653Fura9zLA1znBz+zpdKeF6MiAjVxdJwyMnJlt1f8ALDD6VRhkUP5haceIM4vsqug0HFacNs9ze6pf7lbh7taeDvldEoySUVFDjkNw3MdbzKdeijV9bfrvdVS8rCw58Fhi+d9kL9VgQFWBfm7hqZyvdoU6G9RWJAfvU4CqBKC1Q1U5slCpWXm13c2aoXZbFJgoJ/y84u4WEWDl5qZrzXbk/XTr0k6kVXgmd65TZhG9mujQd1bctEFAGiy3C20A/yqXhaWbl0QHOSn4ee10fETOcrNL1Z+od1z8yW/0FGmezl7SXdzRSVdz7l+Sv4udi3jcDpV0f1aQ6e632vK3EP7eUI6JTekyixTaoLTkAynUW4w3TAkh2HIIUPy3pB+ZzBJspZ0LX88q0A3jO5Sex8GoEHqEBOmP1zZUzeM6aq125P13S/HdOxErn7clqQftyWpbcsQXXxeaw3u0VKhQX71nV00UXV5L497a0DTYzGbFRRgVlCArcZpWa1mNWsWfNYAOQDUFYLVAHCagiK7Nu1O1U+/JmlPYqZneoCfRUN7Rmvk+W3ULjq0/jIIAIAPK926IDIsQD07RqlVRIDXbpKUHm/O/d7dpbbn1TCqNIbiWW/9GiWBXU8LBNeNn/DwIGVn58vhcJYEh00ySyVBYpMnGOxuBWhyB49L0nHfPPrih31Kz84vCSaX+l0qmFx6PfeE0sFnb7U0dI9d5+6m3Ok89d5wuva7e5lTY/05PQ8IuB5ScB+HkumlWnfYS40DWHq8v5Ld7HlAYf32JF0/qrNXtgmA7wkJtOnSQW11ycBYJRzN0uotx7Rxt6u19X+/idfiFXvVt3OUhvWK0Xldmstmbdjj+QIAAAAgWA0AkiS7w6ldhzK0fmeKNu9JVWGxq3mQSVLPDs00vE8r9Y9rIX9aUQMA0KDV1nhzVeUONAdZTeccgLdazQoL9pO/n0V+1oZx7WEymWQ5PVJeywzDFdAuLglehwb568ZxPeQsttdZHgA0TCaTSV1jI9Q1NkI3XtJVa3cka82vyTqUclJb9p7Qlr0nFBxg1aDuLTW8dyt1bhNGN+EAAABAA0WwGkCT5XA6tftQpjbsStHP8anKLTh14zM6MkgX9onRsF4xigwLqMdcAgAANE0mk0lWq0nWkpaRLZoFKiTQpmyC1QBKCQm06dKBbXXpwLY6mpqjNTuStW5HijJOFmr11mNavfWYmocHaGC3lhrQvYU6tSJwDQAAADQkBKsBNCl2h1PxiZnauPu4Nu9JVU5+sWdeWLCfBnRroWG9YtS5NTcwAAAAAMCXtGkRomtHdtGkiztr9+EMrd2erE17UnUiq0BfbTisrzYcVmSYvwbEtdTA7i3UuU24zNT7AAAAgHpFsBpAo5dbUKxf96Vpa8IJbd+frrzCU61xQoNsGtCtpQZ1b6lubSNkNnOjAgAAAAB8mdlsUs8OkerZIVI3X+bQr/vTtGnPcf2yL03p2YX6ZlOivtmUqPAQP/Xv2kJ9O0epe/tmDPsEAAAA1AOC1QAaHcMwlJyep18S0vRLwgntPZIlp2F45ocE2tQ/roUG9Wip7u0iZDGb6zG3AAAAAIDa4u9n0cDuLTWwe0sVFTu040C6Nu05rq0JJ5SVU6RVW45q1ZajslnN6t6umfp2jlLfzlFqERFY31kHAAAAmgSC1QAahazcIu06mK6dhzK062C60rILy8xv0zxY53Vprn5dmqtT6zBaUAMAAABAE+Nns+j8uBY6P66Fiu1O7TyYrl/2pWnbvhNKzy7Ur/vT9Ov+NP33G6lVVJD6dHK1uI6LjVBQALfQAAAAgNrAlTYAn5RbUKy9R7K062CGdh5K19HU3DLzLWaTurWL8ASoeSoeAAAAAOBms5p1XpfmOq9LcxlGnI6eyNW2fWnati9NCUeylJSWp6S0PH29MVEmk9QhJlTd2zVTj/bN1DU2Qv5+dBkOAAAAeAPBagANnmEYOp6Rr71HspRwNFMJR7N17ETuGcu1iw5Rz/aR6tmBmwcAAAAAgKoxmUyKbRGi2BYhGj+0vXILirXjQLp2HszQ7sMZOp6RrwNJJ3Ug6aSWrT8si9mkDjGh6twmXJ1ah6lz63BFhvnLZKIHLwAAAKC6CFY3MCcy8/XLgQztP5al3LxiSZKrrmOSySSZTvtbkswmk0xm16vZbCr1qjJ/u9al4oSGLzOnUIeST+pQykkdTDqpfceydLLk/6G06GaB6t7e9WR79/bNFBbkVw+5BQAAAAA0JsEBNg3uEa3BPaIlSenZBdp1yBW43n0oU2nZBdp3LFv7jmV71gkP8VOX1uHq0jZc53WLVrMgq/ytPECNhs8wDDmchgzDkNOQnKXfG4YMp+u9a1rJe6chw5AMVwquV/ffhmtKVl6xsnIKK/lkAAAAF4LVDczf3tuipLS8WkvfHcS2mM2yWEyymE2ymk2yWMyymE0l08yylswrvZzF4ppus5hlPe29xWIiEI5qczidOp6Rr6OpuTp8/KQOJefoUMpJZecWnbGs1WJWh1ah6tomXF3ahKtzbDjBaQAAAABArYsMC9AFfVrpgj6tJEmpmflKOJKlfceytO9YthJTcpSVU6TN8anaHJ+qJSsSJEnNwwPUtmWI2rYMUbvoULVrGaKo8ADun+CcGIYhu8OpYocruJxTYFdOXpGKih2y213T7Q6nHA5DDqdTDqfh+XE6DTkcZaeVnldbCoocuu+3vWstfQAA0DgQrG5gLhvcTlv2ntDx9DwV2x0yJBklvzzv5XpC0TW95CnHklen0yj1emb6TqchpyS7wyGd2VC1RqwWk6wWsyuAbTV7/nZNK3lvNcvPalZQoJ8Mp1NmsyvgbbOeCoBTaWt8CosdSs3I17G0XB07katjaXlKOpGr5PQ8Ocr5oppMUuuoYLWLdlXoO7cJV/voUNms5nrIPQAAAAAAp7SICFSLiEAN6x0jyVXnPZTs6hVs/7FsJR7P0fGMfJ3IKtCJrAJt2XvCs26An0UxkUFqFRWkmKhgtSp537JZEHXeRszhdMpuN1TscJYElp2uwLPdKbvj1HT3NNd8o8yydkftBZVLM5lKenH0vJpkNpd6b3L13GgynerBsaQTSLn/MpkkP5tFlwxuVyd5BgAAvq1JBqsPHDigl19+WZs3b1ZaWppiYmI0btw43XXXXQoKCqrXvI0ZEKtrLummj1fGKy0zv0ZpGeUGsV1/O0o/UekwZHeeeu9wOkumlbOco+xFst3ulPtS2e4wZHc4VCBHjfLtCXpbzZ7gd3CgTcfS8mSWFGAzK9DfetqP5dR7PysVvDpmdziVebJQ6ScLlZqZX+qnQKmZ+coqp6W0m5/NrFZRwWrXMkTtY0LVPjpUsS1D5G+juzTAV3mznE1JSdGCBQu0Zs0aJScnq3nz5ho9erTuu+8+RUZG1tIWAACAc9GQ69pAbfK3WRTXNkJxbSNktZrVrFmwjhzL1IFj2Tp8PEeJx08qMSVHR0/kqqDIoYPJJ3Uw+WSZNEwmKSosQC0iAtU8PMD143kfqPAQP5l5uL/OOQ1DhUUO5RfaS34cyiu0q6DIrrySaYVFDjlNJu06kK7c/CJX8Ll0kNnuLLdBybkym1TSGMQiy+k9IFrNrl4SPb0nlv4xy1zONPd7s7lsALqmoiICNbR3K2Vk5HolPXgf5TYAoKFocsHqbdu26ZZbblFeXp769u2rPn366Oeff9Yrr7yiVatW6d1331VISEh9Z9Mr3E85mmWSainuZ5QEwt1dDdk9T4iW+rsksF1sP/XefbFf+olSd6txT9C7qFTQOzNfB5Kyy89EOawW82kBbNf7IH+rAkqmud5bFFQqyF16HX8/S5OuCDqdhnIKipWTV6yTeUXKyS/WyfxincwtUkZOUUlwukCZJwuVXc540qcL9LeqdVSQWjUPVuuoYLVuHqzWzYMUGRbQpPcz0Nh4s5xNTEzUjTfeqNTUVMXFxWnUqFHauXOnFi1apG+//VZLlixRTExMLW8RAACoiqZU1waqIjjQpu7tm6l7+2aeaXaHUykZ+UpOc/U0lpTm+klOz1V+ocPTErs8FrNJYcF+Cg/2U0SIv8JDyr6PCPFXaKBNwYE2BfhZmnyvdQ6nUwUlQeaCQofyi+yn/i4VfC4zrejUsp75hQ55sz1zeb0Slulx0FoyryTwXHpZ12tJ0NlsUmCgn/Lzi2q1G280XpTbAICGpEkFq+12u/7v//5PeXl5mj17tq655hpJUkFBgR5++GGtXLlS8+bN08yZM+s5p77DZCp5UtMiVTUiXt4F9RlB75IAtjuY7ednVZe2zZSRlafc/GLlF5Z6qrWkYpFX8jSr5KoAnsxz6mQVgqgVbptUEtgu22rbz+bqytxmtbhebWb5Wy2y2VxPtdqspeaXLOtns3ieUi3zRKv7vcUks8kkq8VUMq542e7QjZJu4GWU6gJerm7h3WMW2Uu3fC+vGymHU4XFrocAioqdktmsjKw85RW49mFBSaUsJ79YOfnFys0vrlaFzGoxKSLE39MdWosI1xPhLZu5/g4OsJ3zsQDgG7xdzk6dOlWpqam677779OCDD0qSHA6HZs2apcWLF+uJJ57Qq6++WmvbAwAAqoa6NlA1VotZbZoHq03z4DLTDcNQZk6RUjPzdSIrXycyC0oC166uxNOzC+VwGso4WaiMk4WSTpb/ASXMJpOCAqwKDrAqONCmoACrQgJcr/5+Fvnbyv742Szy9zO73pe01nXdqzDLaj713mwq21q3svsWrmmn5tkdp3rvs5f08Gd3uP6WSQrMLFBGZp6KihyyOwwV2V33LtyvhcUOFRWXvLeXel/sKLNMYUnQuajY6cUj53pYoEzPfn6nevsLDrSqWXigDhzNUlGRvVRgufSrK0jd1B8iQMNAuQ0AaGiaVLD6iy++UGJiooYNG+YphCUpICBAc+bM0ejRo/X+++/roYceUnh4eD3mtOk5W9A7KiJQE0fHKSMjV3Z7xRUOp9NQQZHdE8wu2zWTQwXuaSXdNuWf1nWTez2H01W1cgfEpcLa2vQGLzjAqpBAm0KD/BQSaFNIkE3NQvzVLLTsT0igjUoX0MR5s5zdtGmTNm3apA4dOuj+++/3TLdYLJoxY4a+//57rV69WgkJCerSpUutbRMAADg76tpAzZhMJk/dOq5txBnz7Q6nsnOLlJVbpMycQmXllLzmFikrp0hZuYXKzHH1iFZsd8ppGJ4H0ZVRsyHmKs235NVWx7XBZjUr0M+iAH+rAvwsniBzgL/rfUDJPHePfO6/g0reu3vjs1krDjS7u373xpB+QF2g3AYANDRNKli9cuVKSdKll156xrxmzZppyJAhWrVqlX744QddeeWVdZ09eIHZbFJQgE1BNWjF626pnFe69XapYLb7id1iu0NFdqeK7U7XE712p+vv4jOnF9sdsjtLxg53Gp73jpIu0b3F3Z3UqTG/y3Yv5Wc9Nd53RFiATDLkb7W4KmwlFbGQQFtJUNpPIYFWWcyM/w2garxZzrrTGjNmjMynnYdsNptGjx6tRYsWacWKFQSrAQCoZ9S1gdpltZgVGRagyLCAsy5bVOxQboFdeQXFyi2wK7egWLn5rr9zCuwqKna4WiCXtEJ2/e08Na3Y4blf4XAacjidcjiMcoPS53I3wyS5WmyX3K+wWEyyms3y87PILHl6n/Ozulp8+9nMntbf7p7r/G3uV9c0z3ybWQF+1jLBaauFexrA6Si3AQANTZMKVsfHx0uSunXrVu78Ll26aNWqVdq9ezcFcRNmMplks1oUbrUoPNiv1j/PMFwB61MVQdeP5KrEud+YSvL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-      "text/plain": [
-       "<Figure size 2000x3000 with 37 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "## Set the data and check different visualizations\n",
-    "data = filtered_df_stable\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
-    "# Set up the figure and axes\n",
-    "fig, axes = plt.subplots(10, 4, figsize=(20, 30))\n",
-    "\n",
-    "# Flatten the axes\n",
-    "axes = axes.flatten()\n",
-    "\n",
-    "# Remove the extra subplots\n",
-    "for i in range(len(data.columns)-1, 40):\n",
-    "    fig.delaxes(axes[i])\n",
-    "\n",
-    "# Create histograms for all score columns\n",
-    "for i, col in enumerate(data.columns[1:]):\n",
-    "    sns.histplot(data[col], ax=axes[i], kde=True)\n",
-    "    axes[i].set_title(col)\n",
-    "\n",
-    "# Improve layout\n",
-    "plt.tight_layout()\n",
-    "plt.savefig(f\"{data_folder}/relevantDrugTargets_scores.pdf\", dpi=300)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "98cb72de-32da-45bb-9ca0-39b0c3449a7c",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
-   "source": [
-    "The box plot visualization represents the distribution of scores across all 38 features. Each box represents the interquartile range (IQR), the line within the box is the median, and the whiskers represent the range within 1.5 times the IQR from the first and third quartiles. Observations outside this range are considered outliers and are represented by individual points.\n",
-    "\n",
-    "From the plot, we can observe that:\n",
-    "\n",
-    "    The median of most features is at or near 0, consistent with our observation from the histograms.\n",
-    "    Many features have many outliers, both on the positive and negative sides.\n",
-    "    The range of scores varies between features. Some features have scores mostly concentrated around 0, while others have a      broader range."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 158,
-   "id": "d01167e4-45a1-4cf1-947c-10a10f8fb571",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ACHE</th>\n",
-       "      <th>ADORA2A</th>\n",
-       "      <th>AHR</th>\n",
-       "      <th>SLC25A4</th>\n",
-       "      <th>ANXA2</th>\n",
-       "      <th>ANXA4</th>\n",
-       "      <th>CACNB1</th>\n",
-       "      <th>COL3A1</th>\n",
-       "      <th>CPT2</th>\n",
-       "      <th>DNMT1</th>\n",
-       "      <th>...</th>\n",
-       "      <th>PMEL</th>\n",
-       "      <th>SLC1A5</th>\n",
-       "      <th>VAMP2</th>\n",
-       "      <th>TOP2A</th>\n",
-       "      <th>TYK2</th>\n",
-       "      <th>TYMS</th>\n",
-       "      <th>VWF</th>\n",
-       "      <th>CACNA2D2</th>\n",
-       "      <th>RAMP1</th>\n",
-       "      <th>SCN3B</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>circuit_name</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Fanconi anemia pathway: RAD51</th>\n",
-       "      <td>-0.521874</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.563521</td>\n",
-       "      <td>-0.241056</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.0000</td>\n",
-       "      <td>-0.0</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.248767</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.013489</td>\n",
-       "      <td>-0.588002</td>\n",
-       "      <td>0.570760</td>\n",
-       "      <td>-0.0</td>\n",
-       "      <td>0.535007</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-0.534459</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Fanconi anemia pathway: RAD51C</th>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.0000</td>\n",
-       "      <td>-0.0</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.042820</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.587580</td>\n",
-       "      <td>-0.0</td>\n",
-       "      <td>0.477307</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.917447</td>\n",
-       "      <td>-0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Fanconi anemia pathway: FANCI</th>\n",
-       "      <td>-0.526704</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.401231</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.390779</td>\n",
-       "      <td>-0.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.397596</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.787002</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.727428</td>\n",
-       "      <td>-0.0</td>\n",
-       "      <td>0.619025</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.749312</td>\n",
-       "      <td>-0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Fanconi anemia pathway: BRCA1</th>\n",
-       "      <td>-0.621120</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.064304</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.047778</td>\n",
-       "      <td>-0.4839</td>\n",
-       "      <td>-0.0</td>\n",
-       "      <td>-0.022072</td>\n",
-       "      <td>-0.071079</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.136791</td>\n",
-       "      <td>0.055904</td>\n",
-       "      <td>-0.635303</td>\n",
-       "      <td>0.615792</td>\n",
-       "      <td>-0.0</td>\n",
-       "      <td>0.757284</td>\n",
-       "      <td>-0.447168</td>\n",
-       "      <td>-0.465851</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Fanconi anemia pathway: FANCM C19orf40</th>\n",
-       "      <td>-0.644022</td>\n",
-       "      <td>-0.457955</td>\n",
-       "      <td>0.108820</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.112910</td>\n",
-       "      <td>-0.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.112814</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.389657</td>\n",
-       "      <td>-0.493088</td>\n",
-       "      <td>0.616645</td>\n",
-       "      <td>-0.0</td>\n",
-       "      <td>0.737936</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.961777</td>\n",
-       "      <td>-0.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 38 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                            ACHE   ADORA2A       AHR  \\\n",
-       "circuit_name                                                           \n",
-       "Fanconi anemia pathway: RAD51          -0.521874 -0.000000 -0.000000   \n",
-       "Fanconi anemia pathway: RAD51C         -0.000000 -0.000000 -0.000000   \n",
-       "Fanconi anemia pathway: FANCI          -0.526704 -0.000000  0.401231   \n",
-       "Fanconi anemia pathway: BRCA1          -0.621120 -0.000000 -0.064304   \n",
-       "Fanconi anemia pathway: FANCM C19orf40 -0.644022 -0.457955  0.108820   \n",
-       "\n",
-       "                                         SLC25A4     ANXA2     ANXA4  CACNB1  \\\n",
-       "circuit_name                                                                   \n",
-       "Fanconi anemia pathway: RAD51          -0.563521 -0.241056 -0.000000 -0.0000   \n",
-       "Fanconi anemia pathway: RAD51C         -0.000000 -0.000000 -0.000000 -0.0000   \n",
-       "Fanconi anemia pathway: FANCI          -0.000000  0.000000  0.390779 -0.0000   \n",
-       "Fanconi anemia pathway: BRCA1          -0.000000 -0.000000 -0.047778 -0.4839   \n",
-       "Fanconi anemia pathway: FANCM C19orf40 -0.000000 -0.000000  0.112910 -0.0000   \n",
-       "\n",
-       "                                        COL3A1      CPT2     DNMT1  ...  \\\n",
-       "circuit_name                                                        ...   \n",
-       "Fanconi anemia pathway: RAD51             -0.0 -0.000000 -0.248767  ...   \n",
-       "Fanconi anemia pathway: RAD51C            -0.0 -0.000000 -0.000000  ...   \n",
-       "Fanconi anemia pathway: FANCI              0.0  0.397596  0.000000  ...   \n",
-       "Fanconi anemia pathway: BRCA1             -0.0 -0.022072 -0.071079  ...   \n",
-       "Fanconi anemia pathway: FANCM C19orf40     0.0  0.112814 -0.000000  ...   \n",
-       "\n",
-       "                                            PMEL    SLC1A5     VAMP2  \\\n",
-       "circuit_name                                                           \n",
-       "Fanconi anemia pathway: RAD51          -0.000000 -0.013489 -0.588002   \n",
-       "Fanconi anemia pathway: RAD51C         -0.000000  0.042820 -0.000000   \n",
-       "Fanconi anemia pathway: FANCI           0.000000  0.787002 -0.000000   \n",
-       "Fanconi anemia pathway: BRCA1          -0.136791  0.055904 -0.635303   \n",
-       "Fanconi anemia pathway: FANCM C19orf40  0.000000  0.389657 -0.493088   \n",
-       "\n",
-       "                                           TOP2A  TYK2      TYMS       VWF  \\\n",
-       "circuit_name                                                                 \n",
-       "Fanconi anemia pathway: RAD51           0.570760  -0.0  0.535007 -0.000000   \n",
-       "Fanconi anemia pathway: RAD51C          0.587580  -0.0  0.477307 -0.000000   \n",
-       "Fanconi anemia pathway: FANCI           0.727428  -0.0  0.619025 -0.000000   \n",
-       "Fanconi anemia pathway: BRCA1           0.615792  -0.0  0.757284 -0.447168   \n",
-       "Fanconi anemia pathway: FANCM C19orf40  0.616645  -0.0  0.737936 -0.000000   \n",
-       "\n",
-       "                                        CACNA2D2     RAMP1     SCN3B  \n",
-       "circuit_name                                                          \n",
-       "Fanconi anemia pathway: RAD51          -0.000000 -1.000000 -0.534459  \n",
-       "Fanconi anemia pathway: RAD51C         -0.000000 -0.917447 -0.000000  \n",
-       "Fanconi anemia pathway: FANCI           0.000000 -0.749312 -0.000000  \n",
-       "Fanconi anemia pathway: BRCA1          -0.465851 -1.000000 -0.000000  \n",
-       "Fanconi anemia pathway: FANCM C19orf40 -0.000000 -0.961777 -0.000000  \n",
-       "\n",
-       "[5 rows x 38 columns]"
-      ]
-     },
-     "execution_count": 158,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 159,
-   "id": "500e3622-8859-4370-9d85-665a8560c416",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 2000x1200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Melt the DataFrame to make it suitable for boxplots\n",
-    "data.reset_index(inplace=True) ## Reset the index back to a column\n",
-    "melted_data = data.melt(id_vars = \"circuit_name\")\n",
-    "\n",
-    "# Compute median of each column\n",
-    "medians = melted_data.groupby(\"variable\")[\"value\"].median().sort_values(ascending=False)\n",
-    "\n",
-    "# Order the data based on medians\n",
-    "melted_data[\"variable\"] = pd.Categorical(melted_data[\"variable\"], categories=medians.index, ordered=True)\n",
-    "\n",
-    "# Create the boxplots\n",
-    "plt.rcParams['font.family'] = 'DejaVu Sans'\n",
-    "plt.figure(figsize=(20, 12), facecolor='white')\n",
-    "sns.set_style(\"whitegrid\")  # set Seaborn style to white grid\n",
-    "sns.boxplot(x=\"variable\", y=\"value\", data = melted_data)\n",
-    "plt.xticks(rotation=40, ha = \"right\")\n",
-    "plt.title(\"Boxplots of SHAP-Scores\")\n",
-    "plt.ylabel('SHAP Scores')\n",
-    "plt.xlabel('Drug-target')\n",
-    "plt.tight_layout()\n",
-    "plt.savefig(f\"{data_folder}/relevant_drugTargetscores_boxplots.pdf\", dpi=300)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 160,
-   "id": "c92a32bd-4e63-43ef-89bf-e9d29f4eab0f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                        drug drug_action    KDT  KDT_name Drug_effect  \\\n",
-      "10               Neostigmine   inhibitor     43      ACHE   Inhibitor   \n",
-      "11            Pyridostigmine   inhibitor     43      ACHE   Inhibitor   \n",
-      "12               Edrophonium   inhibitor     43      ACHE   Inhibitor   \n",
-      "13                Demecarium   inhibitor     43      ACHE   Inhibitor   \n",
-      "14    Gallamine triethiodide   inhibitor     43      ACHE   Inhibitor   \n",
-      "...                      ...         ...    ...       ...         ...   \n",
-      "2570         Lonoctocog alfa      binder   7450       VWF      Ligand   \n",
-      "2571         Susoctocog alfa     binding   7450       VWF      Ligand   \n",
-      "2617              Isradipine   inhibitor   9254  CACNA2D2   Inhibitor   \n",
-      "2648             Pramlintide     agonist  10267     RAMP1   Activator   \n",
-      "2701              Zonisamide   inhibitor  55800     SCN3B   Inhibitor   \n",
-      "\n",
-      "                       drugKDT  \n",
-      "10               Neostigmine43  \n",
-      "11            Pyridostigmine43  \n",
-      "12               Edrophonium43  \n",
-      "13                Demecarium43  \n",
-      "14    Gallamine triethiodide43  \n",
-      "...                        ...  \n",
-      "2570       Lonoctocog alfa7450  \n",
-      "2571       Susoctocog alfa7450  \n",
-      "2617            Isradipine9254  \n",
-      "2648          Pramlintide10267  \n",
-      "2701           Zonisamide55800  \n",
-      "\n",
-      "[102 rows x 6 columns]\n"
-     ]
-    }
-   ],
-   "source": [
-    "### Now lets create a  heatmap with the drugs\n",
-    "\n",
-    "# Load the new data\n",
-    "drugs = pd.read_csv(\"/home/m3m/INFO_PROYECTO/drexml_paper_paquete/data/drugbank-v050110_alldrugbyaction.tsv\", sep='\\t')\n",
-    "\n",
-    "# List of genes in the initial dataset\n",
-    "genes = data.columns[1:].tolist()\n",
-    "\n",
-    "# Filter the drugs dataset to keep only those rows where the gene is in our list\n",
-    "drugs_filtered = drugs[drugs['KDT_name'].isin(genes)]\n",
-    "\n",
-    "# Display the first few rows of the filtered dataset\n",
-    "print(drugs_filtered)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 161,
-   "id": "006c47c0-2ed6-43a2-9540-72be3f2db978",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Calculate the mean absolute score for each gene\n",
-    "mean_scores = data.drop(columns=\"circuit_name\").abs().mean()\n",
-    "\n",
-    "# Copy the DataFrame\n",
-    "drugs_filtered_scores = drugs_filtered.copy()\n",
-    "\n",
-    "# Map the mean scores to the drugs that target each gene\n",
-    "drugs_filtered_scores['mean_score'] = drugs_filtered_scores['KDT_name'].map(mean_scores)\n",
-    "\n",
-    "# Pivot the table to create a matrix of drugs and their mean scores for each gene\n",
-    "drug_gene_scores = drugs_filtered_scores.pivot_table(index='drug', columns='KDT_name', values='mean_score')\n",
-    "\n",
-    "# Replace NaN values with 0\n",
-    "drug_gene_scores = drug_gene_scores.fillna(0)\n",
-    "\n",
-    "# Display the first few rows of the dataset\n",
-    "drug_gene_scores.head()\n",
-    "\n",
-    "drug_gene_scores.to_csv(f\"{data_folder}/drug_KDT_scores.tsv\", sep='\\t', index=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "id": "ec34c062-7c38-49dc-9e4c-aaa7d69b2a62",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1600x1200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Create a heatmap of the drug-gene scores\n",
-    "sns.set(font_scale= 0.5) # Adjust the font size if necessary\n",
-    "\n",
-    "plt.figure(figsize=(16, 12))\n",
-    "ax = sns.heatmap(drug_gene_scores, cmap='Purples', linecolor='black', linewidths=0.5, cbar_kws={'label': 'Mean Absolute Score'})\n",
-    "\n",
-    "# Set the yticks to each row and the yticklabels to the index of the DataFrame\n",
-    "ax.set_yticks([x + 0.5 for x in range(drug_gene_scores.shape[0])])  # shift the yticks by 0.5\n",
-    "ax.set_yticklabels(drug_gene_scores.index, size='small')\n",
-    "\n",
-    "# Ensure that the ticks on the y-axis are visible\n",
-    "ax.yaxis.tick_left()\n",
-    "\n",
-    "plt.title(\"Heatmap of Mean Absolute relevance score of Drug-Targets annotated with their targeting drug\", size)\n",
-    "plt.xticks(rotation=90, ha = \"center\")\n",
-    "plt.savefig(f\"{data_folder}/relevant_drugTargets_withdrugs.pdf\", dpi=300)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 162,
-   "id": "8eeec3f5-8bb1-42d7-b030-9a7f34ba0a9d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Fanconi anemia pathway: RAD51</th>\n",
-       "      <th>Fanconi anemia pathway: RAD51C</th>\n",
-       "      <th>Fanconi anemia pathway: FANCI</th>\n",
-       "      <th>Fanconi anemia pathway: BRCA1</th>\n",
-       "      <th>Fanconi anemia pathway: FANCM C19orf40</th>\n",
-       "      <th>Fanconi anemia pathway: FANCM STRA13</th>\n",
-       "      <th>Cell cycle: SMC1B SMC3 STAG1 RAD21</th>\n",
-       "      <th>Oocyte meiosis: REC8*</th>\n",
-       "      <th>Progesterone-mediated oocyte maturation: ANAPC10</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Neostigmine</th>\n",
-       "      <td>0.521874</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.526704</td>\n",
-       "      <td>0.62112</td>\n",
-       "      <td>0.644022</td>\n",
-       "      <td>0.690006</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.542976</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Pyridostigmine</th>\n",
-       "      <td>0.521874</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.526704</td>\n",
-       "      <td>0.62112</td>\n",
-       "      <td>0.644022</td>\n",
-       "      <td>0.690006</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.542976</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Edrophonium</th>\n",
-       "      <td>0.521874</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.526704</td>\n",
-       "      <td>0.62112</td>\n",
-       "      <td>0.644022</td>\n",
-       "      <td>0.690006</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.542976</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Demecarium</th>\n",
-       "      <td>0.521874</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.526704</td>\n",
-       "      <td>0.62112</td>\n",
-       "      <td>0.644022</td>\n",
-       "      <td>0.690006</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.542976</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Gallamine triethiodide</th>\n",
-       "      <td>0.521874</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.526704</td>\n",
-       "      <td>0.62112</td>\n",
-       "      <td>0.644022</td>\n",
-       "      <td>0.690006</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.542976</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                        Fanconi anemia pathway: RAD51  \\\n",
-       "Neostigmine                                  0.521874   \n",
-       "Pyridostigmine                               0.521874   \n",
-       "Edrophonium                                  0.521874   \n",
-       "Demecarium                                   0.521874   \n",
-       "Gallamine triethiodide                       0.521874   \n",
-       "\n",
-       "                        Fanconi anemia pathway: RAD51C  \\\n",
-       "Neostigmine                                        0.0   \n",
-       "Pyridostigmine                                     0.0   \n",
-       "Edrophonium                                        0.0   \n",
-       "Demecarium                                         0.0   \n",
-       "Gallamine triethiodide                             0.0   \n",
-       "\n",
-       "                        Fanconi anemia pathway: FANCI  \\\n",
-       "Neostigmine                                  0.526704   \n",
-       "Pyridostigmine                               0.526704   \n",
-       "Edrophonium                                  0.526704   \n",
-       "Demecarium                                   0.526704   \n",
-       "Gallamine triethiodide                       0.526704   \n",
-       "\n",
-       "                        Fanconi anemia pathway: BRCA1  \\\n",
-       "Neostigmine                                   0.62112   \n",
-       "Pyridostigmine                                0.62112   \n",
-       "Edrophonium                                   0.62112   \n",
-       "Demecarium                                    0.62112   \n",
-       "Gallamine triethiodide                        0.62112   \n",
-       "\n",
-       "                        Fanconi anemia pathway: FANCM C19orf40  \\\n",
-       "Neostigmine                                           0.644022   \n",
-       "Pyridostigmine                                        0.644022   \n",
-       "Edrophonium                                           0.644022   \n",
-       "Demecarium                                            0.644022   \n",
-       "Gallamine triethiodide                                0.644022   \n",
-       "\n",
-       "                        Fanconi anemia pathway: FANCM STRA13  \\\n",
-       "Neostigmine                                         0.690006   \n",
-       "Pyridostigmine                                      0.690006   \n",
-       "Edrophonium                                         0.690006   \n",
-       "Demecarium                                          0.690006   \n",
-       "Gallamine triethiodide                              0.690006   \n",
-       "\n",
-       "                        Cell cycle: SMC1B SMC3 STAG1 RAD21  \\\n",
-       "Neostigmine                                            0.0   \n",
-       "Pyridostigmine                                         0.0   \n",
-       "Edrophonium                                            0.0   \n",
-       "Demecarium                                             0.0   \n",
-       "Gallamine triethiodide                                 0.0   \n",
-       "\n",
-       "                        Oocyte meiosis: REC8*  \\\n",
-       "Neostigmine                          0.542976   \n",
-       "Pyridostigmine                       0.542976   \n",
-       "Edrophonium                          0.542976   \n",
-       "Demecarium                           0.542976   \n",
-       "Gallamine triethiodide               0.542976   \n",
-       "\n",
-       "                        Progesterone-mediated oocyte maturation: ANAPC10  \n",
-       "Neostigmine                                                          0.0  \n",
-       "Pyridostigmine                                                       0.0  \n",
-       "Edrophonium                                                          0.0  \n",
-       "Demecarium                                                           0.0  \n",
-       "Gallamine triethiodide                                               0.0  "
-      ]
-     },
-     "execution_count": 162,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Filter the drugs dataset to keep only those rows where the gene is in our list\n",
-    "genes = data.columns[1:].tolist()\n",
-    "drugs_filtered = drugs[drugs['KDT_name'].isin(genes)]\n",
-    "\n",
-    "# Create a DataFrame to hold the mean scores for each drug-circuit pair\n",
-    "drug_circuit_scores = pd.DataFrame(index=drugs_filtered['drug'].unique(), columns=data['circuit_name'].unique())\n",
-    "\n",
-    "# Get the absolute scores for each circuit and gene\n",
-    "abs_scores = data.set_index(\"circuit_name\").abs()\n",
-    "\n",
-    "# Create a mapping from drug to genes\n",
-    "drug_genes = drugs_filtered.groupby('drug')['KDT_name'].apply(list).to_dict()\n",
-    "\n",
-    "# Calculate the mean scores for each drug-circuit pair\n",
-    "for drug, genes in drug_genes.items():\n",
-    "    for circuit in drug_circuit_scores.columns:\n",
-    "        # Get the scores for the genes that are in both the drug's targets and the circuit\n",
-    "        scores = abs_scores.loc[circuit, genes]\n",
-    "        # Calculate the mean score\n",
-    "        drug_circuit_scores.loc[drug, circuit] = scores.mean()\n",
-    "\n",
-    "# Convert the scores to floats\n",
-    "drug_circuit_scores = drug_circuit_scores.astype(float)\n",
-    "\n",
-    "drug_circuit_scores.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 165,
-   "id": "ef8d33f8-c96c-4f64-b5fb-64b126c7a9a4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                        drug drug_action    KDT  KDT_name Drug_effect  \\\n",
-      "10               Neostigmine   inhibitor     43      ACHE   Inhibitor   \n",
-      "11            Pyridostigmine   inhibitor     43      ACHE   Inhibitor   \n",
-      "12               Edrophonium   inhibitor     43      ACHE   Inhibitor   \n",
-      "13                Demecarium   inhibitor     43      ACHE   Inhibitor   \n",
-      "14    Gallamine triethiodide   inhibitor     43      ACHE   Inhibitor   \n",
-      "...                      ...         ...    ...       ...         ...   \n",
-      "2570         Lonoctocog alfa      binder   7450       VWF      Ligand   \n",
-      "2571         Susoctocog alfa     binding   7450       VWF      Ligand   \n",
-      "2617              Isradipine   inhibitor   9254  CACNA2D2   Inhibitor   \n",
-      "2648             Pramlintide     agonist  10267     RAMP1   Activator   \n",
-      "2701              Zonisamide   inhibitor  55800     SCN3B   Inhibitor   \n",
-      "\n",
-      "                       drugKDT  \n",
-      "10               Neostigmine43  \n",
-      "11            Pyridostigmine43  \n",
-      "12               Edrophonium43  \n",
-      "13                Demecarium43  \n",
-      "14    Gallamine triethiodide43  \n",
-      "...                        ...  \n",
-      "2570       Lonoctocog alfa7450  \n",
-      "2571       Susoctocog alfa7450  \n",
-      "2617            Isradipine9254  \n",
-      "2648          Pramlintide10267  \n",
-      "2701           Zonisamide55800  \n",
-      "\n",
-      "[102 rows x 6 columns]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(drugs_filtered)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 166,
-   "id": "4a82a5f3-c77a-44e7-8a65-f2e662bf91bd",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1600x1200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Create the heatmap\n",
-    "plt.figure(figsize=(16, 12))\n",
-    "sns.heatmap(drug_circuit_scores, cmap='Blues', linewidths=0.5, linecolor='gray', cbar_kws={'label': 'Mean Absolute Score'})\n",
-    "plt.title(\"Heatmap of Mean Absolute Scores for Drug-Circuit Interactions\")\n",
-    "plt.xticks(rotation=30, ha = \"right\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a4e3b77b-892d-40e6-a026-cfd0a7008e99",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/drexml/heatmap_shap_scores.py b/drexml/heatmap_shap_scores.py
index 1f8a7a5..7a053aa 100644
--- a/drexml/heatmap_shap_scores.py
+++ b/drexml/heatmap_shap_scores.py
@@ -10,7 +10,7 @@
 def create_heatmap_KDTsCir_annot(data_folder, assets_folder, filtered_file_name):
     """
     This function reads the filtered shap_relevant_table data generated from the process_data() function  and the annotations 
-    of most common drug-effect over the drug-targets. Then, it creates a heatmap with the drug-targets (annotated with the most
+    of the most common drug-effect over the drug-targets. Then, it creates a heatmap with the drug-targets (annotated with the most
     common drug effect on top) as columns and the circuits as rows, filled  with the SHAP scores obtained. 
     The heatmap is saved to a .pdf file on the same data_folder.
 
@@ -36,17 +36,45 @@ def create_heatmap_KDTsCir_annot(data_folder, assets_folder, filtered_file_name)
         "Activator": "cyan",
         "Ligand": "yellow",
         "other": "grey",
+        "Modulator": "orange"
     }
     drug_action_colors_list = [drug_action_colors[action] for action in annots["drug_action"]]
 
-    sns.set(font_scale= 1.5) # Adjust the font size if necessary
+    # If the number of circuits is greater than 50, group by pathways
+    if filtered_df_stable.shape[0] > 50:
+        # Split the circuit names by ": " to get the pathways
+        filtered_df_stable.index = filtered_df_stable.index.str.split(": ").str[0]
 
-    # Create the clustermap
-    cluster_map = sns.clustermap(filtered_df_stable, cmap="RdBu_r", col_colors=[drug_action_colors_list], linewidths=0.5, cbar_kws={'label': 'SHAP Value'}, figsize=(20,20))
+        # Aggregate by pathway
+        filtered_df_stable = filtered_df_stable.groupby(level=0).mean()
 
-    # Add titles to the x and y axes
-    cluster_map.ax_heatmap.set_xlabel('Drug-target')
-    cluster_map.ax_heatmap.set_ylabel('Circuit name')
+        # Calculate the figure size and the font size based on the number of rows and columns
+        num_rows, num_cols = filtered_df_stable.shape
+        scale_factor = max(num_rows, num_cols) ** 0.5  # adjust this as needed
+        
+        fig_width = max(50, num_cols / scale_factor)  # adjust this as needed
+        fig_height = max(30, num_rows / scale_factor)  # adjust this as needed
+        # font_size = min(5, 300 / max(num_rows, num_cols))  # adjust this as needed
+       
+        # Create the clustermap with adjusted size
+        cluster_map = sns.clustermap(filtered_df_stable, cmap="RdBu_r", col_colors=[drug_action_colors_list], linewidths=0.5, cbar_kws={'label': 'SHAP Value'}, figsize=(fig_width,fig_height))
+        # cluster_map.ax_heatmap.tick_params(axis='both', which='major', labelsize= 0.8)
+        # Add titles to the x and y axes
+        cluster_map.ax_heatmap.set_xlabel('Drug-target')
+        cluster_map.ax_heatmap.set_ylabel('Pathway name')
+
+
+        print("The number of circuits is greater than 50. The heatmap will be created using pathways.")
+    else:
+        print("The number of circuits is less than or equal to 50. The heatmap will be created using circuits.")
+        
+        sns.set(font_scale= 1.5) # Adjust the font size if necessary
+
+        # Create the clustermap with fixed size
+        cluster_map = sns.clustermap(filtered_df_stable, cmap="RdBu_r", col_colors=[drug_action_colors_list], linewidths=0.5, cbar_kws={'label': 'SHAP Value'}, figsize=(20,20))
+        # Add titles to the x and y axes
+        cluster_map.ax_heatmap.set_xlabel('Drug-target')
+        cluster_map.ax_heatmap.set_ylabel('Circuit name')
 
     # Add a title to the color legend
     legend_elements = [
@@ -57,8 +85,8 @@ def create_heatmap_KDTsCir_annot(data_folder, assets_folder, filtered_file_name)
     legend.get_frame().set_facecolor('white')
 
     # Add a title to the entire figure
-    plt.suptitle('Heatmap of circuits and genes', fontsize=30, y=1.05)
-
+    plt.suptitle('Heatmap of Disease Map and relevant Drug-targets', fontsize=30, y=1.05)
+  
     # Save the plot
     plt.savefig(f"{data_folder}/heatmap_circuits_KDT_drugeff.pdf", dpi=300)
     plt.show()
diff --git a/drexml/heatmap_top10KDT_drugs.py b/drexml/heatmap_top10KDT_drugs.py
index f7c09aa..83f2cde 100644
--- a/drexml/heatmap_top10KDT_drugs.py
+++ b/drexml/heatmap_top10KDT_drugs.py
@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 """
-Analysis of top 10 drug-targets by their drugs on a Heatmap for DREXML.
+Analysis of drugs from the top 10 drug-targets on a Heatmap over disease circuits or pathways depending on size.
 """
 
 import pandas as pd
@@ -8,24 +8,23 @@
 import seaborn as sns
 
 
-
-def create_heatmap_drugsCircuit_top10KDTs(KDTs_path, drugs_path, data_path):
+def create_heatmap_drugsCircuit_top10KDTs(data_folder, filtered_file_name, assets_folder):
     """
     This function reads the filtered matrix of shap scores (columns=drug-targets, rows = disease-circuits) and the drug-target-effects data, 
     filters the top 10 best-scored drug-targets from SHAP and creates a heatmap with the drugs from the top 10 scored targets and the circuits.
+    If the number of circuits is greater than 50, the circuits are grouped by their pathways (the first part of the circuit name before ": ").
     
     Args:
-    KDTs_path (str): The path to the shap_scores data file.
-    drugs_path (str): The path to the drug-target interactions file.
-    data_path (str): The path to the data folder where all results from the DREXML model are.
-    
+    data_folder (str): The path to the data folder where all results from the DREXML model are.
+    filtered_file_name (str): The name of the shap scores filtered data file.
+    assets_folder (str): The path to the assets folder where the drug-target interactions file is located.
     
     Returns:
-    A heatmap is displayed and saved in the data_path.
+    A heatmap is displayed and saved in the data folder.
     """
 
     # Read the genes data
-    df = pd.read_csv(KDTs_path, sep='\t', index_col="circuit_name")
+    df = pd.read_csv(f"{data_folder}/{filtered_file_name}", sep='\t', index_col=0)
 
     # Calculate the mean of absolute values for each gene
     mean_scores = df.abs().mean()
@@ -34,7 +33,7 @@ def create_heatmap_drugsCircuit_top10KDTs(KDTs_path, drugs_path, data_path):
     top_10_genes = mean_scores.nlargest(10)
 
     # Read the drugs data
-    df_drugs = pd.read_csv(drugs_path, sep='\t', skiprows=1, names=["Drug", "Action", "KDT", "Gene", "Drug_effect", "drugKDT"])
+    df_drugs = pd.read_csv(f"{assets_folder}/drugbank-v050110_alldrugbyaction.tsv", sep='\t', skiprows=1, names=["Drug", "Action", "KDT", "Gene", "Drug_effect", "drugKDT"])
 
     # Filter the drugs that target the top 10 genes
     df_drugs_top_10_genes = df_drugs[df_drugs['Gene'].isin(top_10_genes.index)]
@@ -54,11 +53,39 @@ def create_heatmap_drugsCircuit_top10KDTs(KDTs_path, drugs_path, data_path):
     # Convert the scores to floats
     df_heatmap_10 = df_heatmap_10.astype(float)
 
+    # If the number of circuits is greater than 50, group by pathways
+    if df_heatmap_10.shape[0] > 50:
+        # Split the circuit names by ": " to get the pathways
+        df_heatmap_10.index = df_heatmap_10.index.str.split(": ").str[0]
+
+        # Aggregate by pathway
+        df_heatmap_10 = df_heatmap_10.groupby(level=0).mean()
+
+        # Calculate the figure size and the font size based on the number of rows and columns
+        num_rows, num_cols = df_heatmap_10.shape
+        scale_factor = max(num_rows, num_cols) ** 0.5  # adjust this as needed
+
+        fig_width = max(20, num_cols / scale_factor)  # adjust this as needed
+        fig_height = max(20, num_rows / scale_factor)  # adjust this as needed
+
+        font_size = 10.0 / scale_factor  # adjust this as needed
+
+        sns.set(font_scale= font_size) 
+
+        plt.figure(figsize=(fig_width, fig_height))
+        plt.ylabel('Pathway')
+
+
+        print("The number of circuits is greater than 50. The heatmap will be created using pathways.")
+    else:
+        print("The number of circuits is less than or equal to 50. The heatmap will be created using circuits.")
+        plt.figure(figsize=(25, 10))
+        sns.set(font_scale= 2) # Adjust the font size if necessary
+        plt.ylabel('Circuit')
+
     # Plot a heatmap
-    plt.figure(figsize=(12, 10))
     sns.heatmap(df_heatmap_10, cmap="Blues", center=0)
-    plt.title('Mean scores of top 10 genes targeted by each drug in each circuit')
+    plt.title('Mean scores of the drugs from the top 10 best-scored drug-targets over the Disease Map ')
     plt.xlabel('Drug')
-    plt.ylabel('Circuit')
-    plt.savefig(f"{data_path}/heatmap_top10KDTdrugs_circuit.pdf", dpi=300)
-    plt.show()
\ No newline at end of file
+    plt.savefig(f"{data_folder}/heatmap_top10KDTdrugs_circuit.pdf", dpi=300)
+    plt.show()
diff --git a/drexml/histograms_shap_scores.py b/drexml/histograms_shap_scores.py
index 36d219a..a5485fe 100644
--- a/drexml/histograms_shap_scores.py
+++ b/drexml/histograms_shap_scores.py
@@ -6,8 +6,9 @@
 import pandas as pd
 import matplotlib.pyplot as plt
 import seaborn as sns
+import numpy as np
 
-def create_histograms(data_folder, filtered_file_name):
+def create_histograms(data_folder, filtered_file_name = "shap_filtered_stability_symbol.tsv"):
     """
     This function reads the shap scores filtered data, creates histograms from the SHAP scores of each drug-target, 
     and saves the histograms to a composed .pdf file.
@@ -17,27 +18,34 @@ def create_histograms(data_folder, filtered_file_name):
     filtered_file_name (str): The name of the  shap scores filtered file.
 
     Returns:
-    None. The histograms are saved as a .pdf file.
+    The histograms are saved as a .pdf file.
     """
-    # Load the previous filtered table
+   # Load the previous filtered table
     data = pd.read_csv(f"{data_folder}/{filtered_file_name}", sep='\t', index_col=0)
 
+    # Number of columns in the data
+    num_cols = len(data.columns[1:])  
+
+    # Calculate the number of rows required
+    num_rows = int(np.ceil(num_cols / 4.0))  
+
     # Set up the figure and axes
-    fig, axes = plt.subplots(10, 4, figsize=(20, 30))
+    fig, axes = plt.subplots(num_rows, 4, figsize=(20, 3*num_rows))
 
     # Flatten the axes
     axes = axes.flatten()
 
     # Remove the extra subplots
-    for i in range(len(data.columns)-1, 40):
+    for i in range(num_cols, num_rows*4):  
         fig.delaxes(axes[i])
 
     # Create histograms for all score columns
     for i, col in enumerate(data.columns[1:]):
         sns.histplot(data[col], ax=axes[i], kde=True)
         axes[i].set_title(col)
+        axes[i].set_xlabel('')  # remove the x-label
 
     # Improve layout
     plt.tight_layout()
     plt.savefig(f"{data_folder}/relevantDrugTargets_scores.pdf", dpi=300)
-    plt.show()
\ No newline at end of file
+    plt.show()
diff --git a/drexml/shap_scores_filtering.py b/drexml/shap_scores_filtering.py
index bbfb126..46a4e83 100644
--- a/drexml/shap_scores_filtering.py
+++ b/drexml/shap_scores_filtering.py
@@ -14,7 +14,7 @@ def process_data(data_folder, stability_threshold = 0.6):
     stability_threshold (float): The stability threshold to be set (we recommend using a value between 0.4-0.7)
  
     Returns:
-    The final dataframe is saved as a .tsv file.
+    None. The final dataframe is saved as a .tsv file.
     """
     # Read the .tsv files into a DataFrames
     shap_values = pd.read_csv(f"{data_folder}/shap_summary_symbol.tsv", sep='\t', index_col=0)
@@ -39,4 +39,4 @@ def process_data(data_folder, stability_threshold = 0.6):
     filtered_df.reset_index(inplace=True)
 
     filtered_df_stable = filtered_df[filtered_df['circuit_name'].isin(stability_00['circuit_name'])]
-    filtered_df_stable.to_csv(f"{data_folder}/shap_filtered_stability_symbol.tsv", sep='\t', index=False)
\ No newline at end of file
+    filtered_df_stable.to_csv(f"{data_folder}/shap_filtered_stability_symbol.tsv", sep='\t', index=False)

From 43c4e0067838303b437630d628bba6c7732135ff Mon Sep 17 00:00:00 2001
From: Carlos Loucera <carlos.loucera@juntadeandalucia.es>
Date: Mon, 31 Jul 2023 16:02:30 +0200
Subject: [PATCH 06/37] Fix style.

---
 drexml/ORA_drugs.py              | 27 ++++-----
 drexml/boxplots_shap_scores.py   | 34 +++++++-----
 drexml/heatmap_shap_scores.py    | 95 +++++++++++++++++++++++---------
 drexml/heatmap_top10KDT_drugs.py | 56 ++++++++++++-------
 drexml/histograms_shap_scores.py | 25 +++++----
 5 files changed, 150 insertions(+), 87 deletions(-)

diff --git a/drexml/ORA_drugs.py b/drexml/ORA_drugs.py
index 927da8c..c70a598 100644
--- a/drexml/ORA_drugs.py
+++ b/drexml/ORA_drugs.py
@@ -10,9 +10,9 @@
 import dotenv
 import numpy as np
 import pandas as pd
-from scipy import stats
 from statsmodels.stats.multitest import multipletests
 
+
 def fdr(pvalues):
     """
     Function to apply Benjamini-Hochberg FDR p-value correction for multiple hypothesis testing.
@@ -44,28 +44,30 @@ def main(db_path, atc_path):
 
     project_root = pathlib.Path(dotenv.find_dotenv()).absolute().parent
     data_folder = project_root.joinpath("data")
-    raw_folder = data_folder.joinpath("raw")
+    data_folder.joinpath("raw")
     final_folder = data_folder.joinpath("final")
     results_folder = project_root.joinpath("results")
     tables_folder = results_folder.joinpath("tables")
     tables_folder.mkdir(parents=True, exist_ok=True)
 
     atc_url = "https://raw.githubusercontent.com/fabkury/atcd/master/WHO%20ATC-DDD%202021-12-03.csv"
-    
+
     # If ATC codes file doesn't exist, download it from the internet
     if not atc_path.exists():
-        with urllib.request.urlopen(atc_url) as response, open(atc_path, "wb") as out_file:
+        with urllib.request.urlopen(atc_url) as response, open(
+            atc_path, "wb"
+        ) as out_file:
             shutil.copyfileobj(response, out_file)
 
     atc_code_name = pd.read_csv(atc_path, usecols=["atc_code", "atc_name"])
 
     # Read data files
     shap_selection_df = pd.read_csv(
-        results_folder.joinpath("ml", "shap_selection_symbol.tsv"), sep="\t", index_col=0
+        results_folder.joinpath("ml", "shap_selection_symbol.tsv"),
+        sep="\t",
+        index_col=0,
     )
-    drugbank_df = pd.read_csv(
-        final_folder.joinpath(db_path), sep="\t"
-    ).assign(
+    drugbank_df = pd.read_csv(final_folder.joinpath(db_path), sep="\t").assign(
         is_selected=lambda x: x.symbol_id.isin(
             shap_selection_df.columns[shap_selection_df.any()]
         )
@@ -73,7 +75,6 @@ def main(db_path, atc_path):
 
     atc_level_to_len = {1: 1, 2: 3, 3: 4, 4: 5}
 
-    results = []
     ora_min_len = 3
 
     # Apply over-representation analysis on selected drugs at different ATC levels
@@ -101,8 +102,6 @@ def main(db_path, atc_path):
         background = tmp_df.drugbank_id.unique()
         drugs_selected = tmp_df.drugbank_id[tmp_df.is_selected].unique()
 
-        ora_dict = {}
-
         # Apply ORA
         for atc_code in atc_dict.keys():
             drug_list_in_atc = np.unique(atc_dict[atc_code])
@@ -110,8 +109,8 @@ def main(db_path, atc_path):
             if n_drug_list_in_atc < ora_min_len:
                 print(f"Ignore ATC codes with less than {ora_min_len} drugs")
             else:
-                selected_drugs_in_atc = np.intersect1d(drugs_selected, drug_list_in_atc)
-                selected_drugs_notin_atc = np.setdiff1d(drugs_selected, drug_list_in_atc)
+                np.intersect1d(drugs_selected, drug_list_in_atc)
+                np.setdiff1d(drugs_selected, drug_list_in_atc)
 
                 drugs_in_atc_not_selected = np.intersect1d(
                     drug_list_in_atc, np.setdiff1d(background, drugs_selected)
@@ -119,5 +118,3 @@ def main(db_path, atc_path):
                 drugs_notin_atc_not_selected = np.setdiff1d(
                     np.setdiff1d(background, drugs_selected), drug_list_in_atc
                 )
-
-                contingency_table = np
\ No newline at end of file
diff --git a/drexml/boxplots_shap_scores.py b/drexml/boxplots_shap_scores.py
index 6bf909f..cc31093 100644
--- a/drexml/boxplots_shap_scores.py
+++ b/drexml/boxplots_shap_scores.py
@@ -3,10 +3,11 @@
 SHAP scores analysis on boxplots for DREXML.
 """
 
-import pandas as pd
 import matplotlib.pyplot as plt
+import pandas as pd
 import seaborn as sns
 
+
 def create_boxplots(data_folder, filtered_file_name):
     """
     This function reads the filtered data, creates boxplots of the scores from the relevant drug-targets on the disease circuits, and saves the boxplots to a .pdf file.
@@ -19,35 +20,38 @@ def create_boxplots(data_folder, filtered_file_name):
     None. The boxplots are saved as a .pdf file.
     """
     # Load the previous filtered table
-    data = pd.read_csv(f"{data_folder}/{filtered_file_name}", sep='\t', index_col=0)
+    data = pd.read_csv(f"{data_folder}/{filtered_file_name}", sep="\t", index_col=0)
 
     # Melt the DataFrame to make it suitable for boxplots
-    data.reset_index(inplace=True) ## Reset the index back to a column
-    melted_data = data.melt(id_vars = "circuit_name")
+    data.reset_index(inplace=True)  ## Reset the index back to a column
+    melted_data = data.melt(id_vars="circuit_name")
 
     # Compute median of each column
-    medians = melted_data.groupby("variable")["value"].median().sort_values(ascending=False)
+    medians = (
+        melted_data.groupby("variable")["value"].median().sort_values(ascending=False)
+    )
 
     # Order the data based on medians
-    melted_data["variable"] = pd.Categorical(melted_data["variable"], categories=medians.index, ordered=True)
-    
+    melted_data["variable"] = pd.Categorical(
+        melted_data["variable"], categories=medians.index, ordered=True
+    )
+
     # Calculate the width size based on the number of variables
     num_variables = melted_data["variable"].nunique()
     fig_width = max(10, num_variables / 2)  # adjust this formula as needed
-    
 
     # Create the boxplots
-    plt.rcParams['font.family'] = 'DejaVu Sans'
-    plt.figure(figsize=(fig_width, 12), facecolor='white')
+    plt.rcParams["font.family"] = "DejaVu Sans"
+    plt.figure(figsize=(fig_width, 12), facecolor="white")
     sns.set_style("whitegrid")  # set Seaborn style to white grid
-    boxplot = sns.boxplot(x="variable", y="value", data = melted_data)
+    boxplot = sns.boxplot(x="variable", y="value", data=melted_data)
     # Set the font size for x tick labels
     # boxplot.set_xticklabels(boxplot.get_xticklabels(), size=15)
-    
-    plt.xticks(rotation=40, ha = "right")
+
+    plt.xticks(rotation=40, ha="right")
     plt.title("Boxplots of SHAP-Scores")
-    plt.ylabel('SHAP Scores')
-    plt.xlabel('Drug-target')
+    plt.ylabel("SHAP Scores")
+    plt.xlabel("Drug-target")
     plt.tight_layout()
     plt.savefig(f"{data_folder}/relevant_drugTargetscores_boxplots.pdf", dpi=300)
     plt.show()
diff --git a/drexml/heatmap_shap_scores.py b/drexml/heatmap_shap_scores.py
index 7a053aa..525b701 100644
--- a/drexml/heatmap_shap_scores.py
+++ b/drexml/heatmap_shap_scores.py
@@ -3,15 +3,16 @@
 SHAP scores analysis on Heatmap for DREXML.
 """
 
+import matplotlib.pyplot as plt
 import pandas as pd
 import seaborn as sns
-import matplotlib.pyplot as plt
+
 
 def create_heatmap_KDTsCir_annot(data_folder, assets_folder, filtered_file_name):
     """
-    This function reads the filtered shap_relevant_table data generated from the process_data() function  and the annotations 
+    This function reads the filtered shap_relevant_table data generated from the process_data() function  and the annotations
     of the most common drug-effect over the drug-targets. Then, it creates a heatmap with the drug-targets (annotated with the most
-    common drug effect on top) as columns and the circuits as rows, filled  with the SHAP scores obtained. 
+    common drug effect on top) as columns and the circuits as rows, filled  with the SHAP scores obtained.
     The heatmap is saved to a .pdf file on the same data_folder.
 
     Args:
@@ -23,22 +24,32 @@ def create_heatmap_KDTsCir_annot(data_folder, assets_folder, filtered_file_name)
     None. The heatmap is saved as a .pdf file.
     """
     # Load the previous filtered table
-    filtered_df_stable = pd.read_csv(f"{data_folder}/{filtered_file_name}", sep='\t', index_col=0)
+    filtered_df_stable = pd.read_csv(
+        f"{data_folder}/{filtered_file_name}", sep="\t", index_col=0
+    )
 
     # Load annotations of the most common drug-effects over the drug-targets
-    annots = pd.read_csv(f"{assets_folder}/drugbank-v050110_mostdrugeffects_KDTs.tsv", sep='\t', index_col=0)
+    annots = pd.read_csv(
+        f"{assets_folder}/drugbank-v050110_mostdrugeffects_KDTs.tsv",
+        sep="\t",
+        index_col=0,
+    )
 
     # Subset the relevant drug-targets
-    annots = annots.loc[annots.index.intersection(filtered_df_stable.columns),]
+    annots = annots.loc[
+        annots.index.intersection(filtered_df_stable.columns),
+    ]
 
     drug_action_colors = {
         "Inhibitor": "magenta",
         "Activator": "cyan",
         "Ligand": "yellow",
         "other": "grey",
-        "Modulator": "orange"
+        "Modulator": "orange",
     }
-    drug_action_colors_list = [drug_action_colors[action] for action in annots["drug_action"]]
+    drug_action_colors_list = [
+        drug_action_colors[action] for action in annots["drug_action"]
+    ]
 
     # If the number of circuits is greater than 50, group by pathways
     if filtered_df_stable.shape[0] > 50:
@@ -51,42 +62,74 @@ def create_heatmap_KDTsCir_annot(data_folder, assets_folder, filtered_file_name)
         # Calculate the figure size and the font size based on the number of rows and columns
         num_rows, num_cols = filtered_df_stable.shape
         scale_factor = max(num_rows, num_cols) ** 0.5  # adjust this as needed
-        
+
         fig_width = max(50, num_cols / scale_factor)  # adjust this as needed
         fig_height = max(30, num_rows / scale_factor)  # adjust this as needed
         # font_size = min(5, 300 / max(num_rows, num_cols))  # adjust this as needed
-       
+
         # Create the clustermap with adjusted size
-        cluster_map = sns.clustermap(filtered_df_stable, cmap="RdBu_r", col_colors=[drug_action_colors_list], linewidths=0.5, cbar_kws={'label': 'SHAP Value'}, figsize=(fig_width,fig_height))
+        cluster_map = sns.clustermap(
+            filtered_df_stable,
+            cmap="RdBu_r",
+            col_colors=[drug_action_colors_list],
+            linewidths=0.5,
+            cbar_kws={"label": "SHAP Value"},
+            figsize=(fig_width, fig_height),
+        )
         # cluster_map.ax_heatmap.tick_params(axis='both', which='major', labelsize= 0.8)
         # Add titles to the x and y axes
-        cluster_map.ax_heatmap.set_xlabel('Drug-target')
-        cluster_map.ax_heatmap.set_ylabel('Pathway name')
-
+        cluster_map.ax_heatmap.set_xlabel("Drug-target")
+        cluster_map.ax_heatmap.set_ylabel("Pathway name")
 
-        print("The number of circuits is greater than 50. The heatmap will be created using pathways.")
+        print(
+            "The number of circuits is greater than 50. The heatmap will be created using pathways."
+        )
     else:
-        print("The number of circuits is less than or equal to 50. The heatmap will be created using circuits.")
-        
-        sns.set(font_scale= 1.5) # Adjust the font size if necessary
+        print(
+            "The number of circuits is less than or equal to 50. The heatmap will be created using circuits."
+        )
+
+        sns.set(font_scale=1.5)  # Adjust the font size if necessary
 
         # Create the clustermap with fixed size
-        cluster_map = sns.clustermap(filtered_df_stable, cmap="RdBu_r", col_colors=[drug_action_colors_list], linewidths=0.5, cbar_kws={'label': 'SHAP Value'}, figsize=(20,20))
+        cluster_map = sns.clustermap(
+            filtered_df_stable,
+            cmap="RdBu_r",
+            col_colors=[drug_action_colors_list],
+            linewidths=0.5,
+            cbar_kws={"label": "SHAP Value"},
+            figsize=(20, 20),
+        )
         # Add titles to the x and y axes
-        cluster_map.ax_heatmap.set_xlabel('Drug-target')
-        cluster_map.ax_heatmap.set_ylabel('Circuit name')
+        cluster_map.ax_heatmap.set_xlabel("Drug-target")
+        cluster_map.ax_heatmap.set_ylabel("Circuit name")
 
     # Add a title to the color legend
     legend_elements = [
-        plt.Line2D([0], [0], marker='o', color='w', markerfacecolor=color, label=action, markersize=15)
+        plt.Line2D(
+            [0],
+            [0],
+            marker="o",
+            color="w",
+            markerfacecolor=color,
+            label=action,
+            markersize=15,
+        )
         for action, color in drug_action_colors.items()
     ]
-    legend = plt.legend(handles=legend_elements, title="Drug Action", loc='upper left', bbox_to_anchor=(15, 1))
-    legend.get_frame().set_facecolor('white')
+    legend = plt.legend(
+        handles=legend_elements,
+        title="Drug Action",
+        loc="upper left",
+        bbox_to_anchor=(15, 1),
+    )
+    legend.get_frame().set_facecolor("white")
 
     # Add a title to the entire figure
-    plt.suptitle('Heatmap of Disease Map and relevant Drug-targets', fontsize=30, y=1.05)
-  
+    plt.suptitle(
+        "Heatmap of Disease Map and relevant Drug-targets", fontsize=30, y=1.05
+    )
+
     # Save the plot
     plt.savefig(f"{data_folder}/heatmap_circuits_KDT_drugeff.pdf", dpi=300)
     plt.show()
diff --git a/drexml/heatmap_top10KDT_drugs.py b/drexml/heatmap_top10KDT_drugs.py
index 83f2cde..3adc24f 100644
--- a/drexml/heatmap_top10KDT_drugs.py
+++ b/drexml/heatmap_top10KDT_drugs.py
@@ -3,28 +3,30 @@
 Analysis of drugs from the top 10 drug-targets on a Heatmap over disease circuits or pathways depending on size.
 """
 
-import pandas as pd
 import matplotlib.pyplot as plt
+import pandas as pd
 import seaborn as sns
 
 
-def create_heatmap_drugsCircuit_top10KDTs(data_folder, filtered_file_name, assets_folder):
+def create_heatmap_drugsCircuit_top10KDTs(
+    data_folder, filtered_file_name, assets_folder
+):
     """
-    This function reads the filtered matrix of shap scores (columns=drug-targets, rows = disease-circuits) and the drug-target-effects data, 
+    This function reads the filtered matrix of shap scores (columns=drug-targets, rows = disease-circuits) and the drug-target-effects data,
     filters the top 10 best-scored drug-targets from SHAP and creates a heatmap with the drugs from the top 10 scored targets and the circuits.
     If the number of circuits is greater than 50, the circuits are grouped by their pathways (the first part of the circuit name before ": ").
-    
+
     Args:
     data_folder (str): The path to the data folder where all results from the DREXML model are.
     filtered_file_name (str): The name of the shap scores filtered data file.
     assets_folder (str): The path to the assets folder where the drug-target interactions file is located.
-    
+
     Returns:
     A heatmap is displayed and saved in the data folder.
     """
 
     # Read the genes data
-    df = pd.read_csv(f"{data_folder}/{filtered_file_name}", sep='\t', index_col=0)
+    df = pd.read_csv(f"{data_folder}/{filtered_file_name}", sep="\t", index_col=0)
 
     # Calculate the mean of absolute values for each gene
     mean_scores = df.abs().mean()
@@ -33,20 +35,29 @@ def create_heatmap_drugsCircuit_top10KDTs(data_folder, filtered_file_name, asset
     top_10_genes = mean_scores.nlargest(10)
 
     # Read the drugs data
-    df_drugs = pd.read_csv(f"{assets_folder}/drugbank-v050110_alldrugbyaction.tsv", sep='\t', skiprows=1, names=["Drug", "Action", "KDT", "Gene", "Drug_effect", "drugKDT"])
+    df_drugs = pd.read_csv(
+        f"{assets_folder}/drugbank-v050110_alldrugbyaction.tsv",
+        sep="\t",
+        skiprows=1,
+        names=["Drug", "Action", "KDT", "Gene", "Drug_effect", "drugKDT"],
+    )
 
     # Filter the drugs that target the top 10 genes
-    df_drugs_top_10_genes = df_drugs[df_drugs['Gene'].isin(top_10_genes.index)]
+    df_drugs_top_10_genes = df_drugs[df_drugs["Gene"].isin(top_10_genes.index)]
 
     # Create a new dataframe where each row corresponds to a drug and a circuit, and the value is the mean score of the top gene targeted by the drug in that circuit
-    df_heatmap_10 = pd.DataFrame(index=df.index.unique(), columns=df_drugs_top_10_genes['Drug'].unique())
+    df_heatmap_10 = pd.DataFrame(
+        index=df.index.unique(), columns=df_drugs_top_10_genes["Drug"].unique()
+    )
 
     # Populate the dataframe with mean scores
     for circuit in df_heatmap_10.index:
         for drug in df_heatmap_10.columns:
             # Get the genes targeted by the drug
-            genes = df_drugs_top_10_genes.loc[df_drugs_top_10_genes['Drug'] == drug, 'Gene'].values
-            
+            genes = df_drugs_top_10_genes.loc[
+                df_drugs_top_10_genes["Drug"] == drug, "Gene"
+            ].values
+
             # Calculate the mean score of the genes in the circuit
             df_heatmap_10.loc[circuit, drug] = df.loc[circuit, genes].abs().mean()
 
@@ -70,22 +81,27 @@ def create_heatmap_drugsCircuit_top10KDTs(data_folder, filtered_file_name, asset
 
         font_size = 10.0 / scale_factor  # adjust this as needed
 
-        sns.set(font_scale= font_size) 
+        sns.set(font_scale=font_size)
 
         plt.figure(figsize=(fig_width, fig_height))
-        plt.ylabel('Pathway')
-
+        plt.ylabel("Pathway")
 
-        print("The number of circuits is greater than 50. The heatmap will be created using pathways.")
+        print(
+            "The number of circuits is greater than 50. The heatmap will be created using pathways."
+        )
     else:
-        print("The number of circuits is less than or equal to 50. The heatmap will be created using circuits.")
+        print(
+            "The number of circuits is less than or equal to 50. The heatmap will be created using circuits."
+        )
         plt.figure(figsize=(25, 10))
-        sns.set(font_scale= 2) # Adjust the font size if necessary
-        plt.ylabel('Circuit')
+        sns.set(font_scale=2)  # Adjust the font size if necessary
+        plt.ylabel("Circuit")
 
     # Plot a heatmap
     sns.heatmap(df_heatmap_10, cmap="Blues", center=0)
-    plt.title('Mean scores of the drugs from the top 10 best-scored drug-targets over the Disease Map ')
-    plt.xlabel('Drug')
+    plt.title(
+        "Mean scores of the drugs from the top 10 best-scored drug-targets over the Disease Map "
+    )
+    plt.xlabel("Drug")
     plt.savefig(f"{data_folder}/heatmap_top10KDTdrugs_circuit.pdf", dpi=300)
     plt.show()
diff --git a/drexml/histograms_shap_scores.py b/drexml/histograms_shap_scores.py
index a5485fe..a106210 100644
--- a/drexml/histograms_shap_scores.py
+++ b/drexml/histograms_shap_scores.py
@@ -3,14 +3,17 @@
 SHAP scores analysis on histograms for DREXML.
 """
 
-import pandas as pd
 import matplotlib.pyplot as plt
-import seaborn as sns
 import numpy as np
+import pandas as pd
+import seaborn as sns
+
 
-def create_histograms(data_folder, filtered_file_name = "shap_filtered_stability_symbol.tsv"):
+def create_histograms(
+    data_folder, filtered_file_name="shap_filtered_stability_symbol.tsv"
+):
     """
-    This function reads the shap scores filtered data, creates histograms from the SHAP scores of each drug-target, 
+    This function reads the shap scores filtered data, creates histograms from the SHAP scores of each drug-target,
     and saves the histograms to a composed .pdf file.
 
     Args:
@@ -20,30 +23,30 @@ def create_histograms(data_folder, filtered_file_name = "shap_filtered_stability
     Returns:
     The histograms are saved as a .pdf file.
     """
-   # Load the previous filtered table
-    data = pd.read_csv(f"{data_folder}/{filtered_file_name}", sep='\t', index_col=0)
+    # Load the previous filtered table
+    data = pd.read_csv(f"{data_folder}/{filtered_file_name}", sep="\t", index_col=0)
 
     # Number of columns in the data
-    num_cols = len(data.columns[1:])  
+    num_cols = len(data.columns[1:])
 
     # Calculate the number of rows required
-    num_rows = int(np.ceil(num_cols / 4.0))  
+    num_rows = int(np.ceil(num_cols / 4.0))
 
     # Set up the figure and axes
-    fig, axes = plt.subplots(num_rows, 4, figsize=(20, 3*num_rows))
+    fig, axes = plt.subplots(num_rows, 4, figsize=(20, 3 * num_rows))
 
     # Flatten the axes
     axes = axes.flatten()
 
     # Remove the extra subplots
-    for i in range(num_cols, num_rows*4):  
+    for i in range(num_cols, num_rows * 4):
         fig.delaxes(axes[i])
 
     # Create histograms for all score columns
     for i, col in enumerate(data.columns[1:]):
         sns.histplot(data[col], ax=axes[i], kde=True)
         axes[i].set_title(col)
-        axes[i].set_xlabel('')  # remove the x-label
+        axes[i].set_xlabel("")  # remove the x-label
 
     # Improve layout
     plt.tight_layout()

From 07d9ac06815757cb156666e2700cbe2c46a0b507 Mon Sep 17 00:00:00 2001
From: Carlos Loucera <carlos.loucera@juntadeandalucia.es>
Date: Mon, 31 Jul 2023 17:34:41 +0200
Subject: [PATCH 07/37] Fix style.

---
 drexml/shap_scores_filtering.py | 37 ++++++++++++++++++++++++---------
 1 file changed, 27 insertions(+), 10 deletions(-)

diff --git a/drexml/shap_scores_filtering.py b/drexml/shap_scores_filtering.py
index 46a4e83..c517705 100644
--- a/drexml/shap_scores_filtering.py
+++ b/drexml/shap_scores_filtering.py
@@ -4,22 +4,29 @@
 """
 import pandas as pd
 
-def process_data(data_folder, stability_threshold = 0.6):
+
+def process_data(data_folder, stability_threshold=0.6):
     """
-    Once the folder where the model results is defined, this function reads the shap_summary (scores) and shap_selection (selected as relevant) .tsv     files into DataFrames, applies Min-Max normalization to make the scores comparable, filters the circuits 
+    Once the folder where the model results is defined, this function reads the shap_summary (scores) and shap_selection (selected as relevant) .tsv     files into DataFrames, applies Min-Max normalization to make the scores comparable, filters the circuits
     based on a desired stability threshold (by default 0.6), and saves the final shap_filtered_stable dataframe to a .tsv file.
 
     Args:
     data_folder (str): The path to the data folder.
     stability_threshold (float): The stability threshold to be set (we recommend using a value between 0.4-0.7)
- 
+
     Returns:
     None. The final dataframe is saved as a .tsv file.
     """
     # Read the .tsv files into a DataFrames
-    shap_values = pd.read_csv(f"{data_folder}/shap_summary_symbol.tsv", sep='\t', index_col=0)
-    shap_selection = pd.read_csv(f"{data_folder}/shap_selection_symbol.tsv", sep='\t', index_col=0)
-    stability = pd.read_csv(f"{data_folder}/stability_results_symbol.tsv", sep='\t', index_col=0) 
+    shap_values = pd.read_csv(
+        f"{data_folder}/shap_summary_symbol.tsv", sep="\t", index_col=0
+    )
+    shap_selection = pd.read_csv(
+        f"{data_folder}/shap_selection_symbol.tsv", sep="\t", index_col=0
+    )
+    stability = pd.read_csv(
+        f"{data_folder}/stability_results_symbol.tsv", sep="\t", index_col=0
+    )
 
     # Define min and max range for normalization
     min_range = -1
@@ -27,16 +34,26 @@ def process_data(data_folder, stability_threshold = 0.6):
 
     # Apply Min-Max normalization to each row
     shap_normalized = shap_values.copy()
-    shap_normalized.iloc[:,1:] = shap_normalized.iloc[:,1:].apply(lambda row: (row - row.min()) / (row.max() - row.min()) * (max_range - min_range) + min_range, axis=1)
+    shap_normalized.iloc[:, 1:] = shap_normalized.iloc[:, 1:].apply(
+        lambda row: (row - row.min())
+        / (row.max() - row.min())
+        * (max_range - min_range)
+        + min_range,
+        axis=1,
+    )
 
     shap_filtered_norm = shap_normalized * shap_selection
     filtered_df = shap_filtered_norm.loc[:, (shap_filtered_norm != 0).sum() != 0]
 
     # Now filter those circuits that have stability above the desired threshold
-    stability_00 = stability[stability['stability'] > stability_threshold]
+    stability_00 = stability[stability["stability"] > stability_threshold]
 
     stability_00.reset_index(inplace=True)
     filtered_df.reset_index(inplace=True)
 
-    filtered_df_stable = filtered_df[filtered_df['circuit_name'].isin(stability_00['circuit_name'])]
-    filtered_df_stable.to_csv(f"{data_folder}/shap_filtered_stability_symbol.tsv", sep='\t', index=False)
+    filtered_df_stable = filtered_df[
+        filtered_df["circuit_name"].isin(stability_00["circuit_name"])
+    ]
+    filtered_df_stable.to_csv(
+        f"{data_folder}/shap_filtered_stability_symbol.tsv", sep="\t", index=False
+    )

From 3666f1032be357d942a350647e760df120bd29d8 Mon Sep 17 00:00:00 2001
From: Carlos Loucera <carlos.loucera@juntadeandalucia.es>
Date: Mon, 31 Jul 2023 17:37:45 +0200
Subject: [PATCH 08/37] Scale by max abs value across circuits.

---
 drexml/shap_scores_filtering.py | 55 +++++++++++++++------------------
 1 file changed, 25 insertions(+), 30 deletions(-)

diff --git a/drexml/shap_scores_filtering.py b/drexml/shap_scores_filtering.py
index c517705..7ccc3a6 100644
--- a/drexml/shap_scores_filtering.py
+++ b/drexml/shap_scores_filtering.py
@@ -3,6 +3,7 @@
 SHAP scores analysis for DREXML.
 """
 import pandas as pd
+from sklearn.preprocessing import maxabs_scale
 
 
 def process_data(data_folder, stability_threshold=0.6):
@@ -17,43 +18,37 @@ def process_data(data_folder, stability_threshold=0.6):
     Returns:
     None. The final dataframe is saved as a .tsv file.
     """
-    # Read the .tsv files into a DataFrames
-    shap_values = pd.read_csv(
-        f"{data_folder}/shap_summary_symbol.tsv", sep="\t", index_col=0
-    )
+
+    # Read the .tsv files into a DataFrames (circuits X genes)
+    shap_values = pd.read_csv(f"{data_folder}/shap_summary.tsv", sep="\t", index_col=0)
     shap_selection = pd.read_csv(
-        f"{data_folder}/shap_selection_symbol.tsv", sep="\t", index_col=0
-    )
-    stability = pd.read_csv(
-        f"{data_folder}/stability_results_symbol.tsv", sep="\t", index_col=0
+        f"{data_folder}/shap_selection.tsv", sep="\t", index_col=0
     )
 
-    # Define min and max range for normalization
-    min_range = -1
-    max_range = 1
-
-    # Apply Min-Max normalization to each row
-    shap_normalized = shap_values.copy()
-    shap_normalized.iloc[:, 1:] = shap_normalized.iloc[:, 1:].apply(
-        lambda row: (row - row.min())
-        / (row.max() - row.min())
-        * (max_range - min_range)
-        + min_range,
-        axis=1,
+    # Read the .tsv files into a DataFrames (circuits X metrics)
+    stability = pd.read_csv(
+        f"{data_folder}/stability_results.tsv", sep="\t", index_col=0
     )
 
-    shap_filtered_norm = shap_normalized * shap_selection
-    filtered_df = shap_filtered_norm.loc[:, (shap_filtered_norm != 0).sum() != 0]
+    shap_selection = shap_selection.loc[:, shap_selection.any()]
 
-    # Now filter those circuits that have stability above the desired threshold
-    stability_00 = stability[stability["stability"] > stability_threshold]
+    circuits_stable = stability[
+        stability["stability"] > stability_threshold
+    ].index.intersection(shap_values.index)
 
-    stability_00.reset_index(inplace=True)
-    filtered_df.reset_index(inplace=True)
+    # circuit-wise scale by max abs value
+    # filter using the selected genes (columns)
+    # filter using stable circuits
+    filtered_df_stable = (
+        shap_values.transform(maxabs_scale)
+        .multiply(shap_selection)
+        .dropna(axis=1)
+        .loc[circuits_stable]
+    )
 
-    filtered_df_stable = filtered_df[
-        filtered_df["circuit_name"].isin(stability_00["circuit_name"])
-    ]
     filtered_df_stable.to_csv(
-        f"{data_folder}/shap_filtered_stability_symbol.tsv", sep="\t", index=False
+        f"{data_folder}/shap_filtered_stability_symbol.tsv",
+        sep="\t",
+        index=True,
+        index_label="circuit_name",
     )

From 053cb64ebc3ac661dfba13414aa6ce0194e319eb Mon Sep 17 00:00:00 2001
From: Carlos Loucera <carlos.loucera@juntadeandalucia.es>
Date: Mon, 31 Jul 2023 17:38:27 +0200
Subject: [PATCH 09/37] Fix file names

---
 drexml/shap_scores_filtering.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/drexml/shap_scores_filtering.py b/drexml/shap_scores_filtering.py
index 7ccc3a6..46b733f 100644
--- a/drexml/shap_scores_filtering.py
+++ b/drexml/shap_scores_filtering.py
@@ -20,14 +20,14 @@ def process_data(data_folder, stability_threshold=0.6):
     """
 
     # Read the .tsv files into a DataFrames (circuits X genes)
-    shap_values = pd.read_csv(f"{data_folder}/shap_summary.tsv", sep="\t", index_col=0)
+    shap_values = pd.read_csv(f"{data_folder}/shap_summary_symbol.tsv", sep="\t", index_col=0)
     shap_selection = pd.read_csv(
-        f"{data_folder}/shap_selection.tsv", sep="\t", index_col=0
+        f"{data_folder}/shap_selection_symbol.tsv", sep="\t", index_col=0
     )
 
     # Read the .tsv files into a DataFrames (circuits X metrics)
     stability = pd.read_csv(
-        f"{data_folder}/stability_results.tsv", sep="\t", index_col=0
+        f"{data_folder}/stability_results_symbol.tsv", sep="\t", index_col=0
     )
 
     shap_selection = shap_selection.loc[:, shap_selection.any()]

From 3f7262d49a91a95b22949471aa6a3187d3054a4c Mon Sep 17 00:00:00 2001
From: marinaEM <47103829+marinaEM@users.noreply.github.com>
Date: Tue, 1 Aug 2023 16:43:58 +0200
Subject: [PATCH 10/37] change fig size heatmap_top10KDT_drugs.py

---
 drexml/heatmap_top10KDT_drugs.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/drexml/heatmap_top10KDT_drugs.py b/drexml/heatmap_top10KDT_drugs.py
index 3adc24f..60dbb88 100644
--- a/drexml/heatmap_top10KDT_drugs.py
+++ b/drexml/heatmap_top10KDT_drugs.py
@@ -93,7 +93,7 @@ def create_heatmap_drugsCircuit_top10KDTs(
         print(
             "The number of circuits is less than or equal to 50. The heatmap will be created using circuits."
         )
-        plt.figure(figsize=(25, 10))
+        plt.figure(figsize=(40,20))
         sns.set(font_scale=2)  # Adjust the font size if necessary
         plt.ylabel("Circuit")
 
@@ -104,4 +104,5 @@ def create_heatmap_drugsCircuit_top10KDTs(
     )
     plt.xlabel("Drug")
     plt.savefig(f"{data_folder}/heatmap_top10KDTdrugs_circuit.pdf", dpi=300)
+    plt.tight_layout()
     plt.show()

From 26db134b23487b118491d7c93054840e6fef7a6d Mon Sep 17 00:00:00 2001
From: marinaEM <47103829+marinaEM@users.noreply.github.com>
Date: Tue, 1 Aug 2023 17:32:40 +0200
Subject: [PATCH 11/37] update threshold and rename heatmap_top10KDT_drugs.py
 to heatmap_top30KDT_drugs.py

---
 ...{heatmap_top10KDT_drugs.py => heatmap_top30KDT_drugs.py} | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)
 rename drexml/{heatmap_top10KDT_drugs.py => heatmap_top30KDT_drugs.py} (96%)

diff --git a/drexml/heatmap_top10KDT_drugs.py b/drexml/heatmap_top30KDT_drugs.py
similarity index 96%
rename from drexml/heatmap_top10KDT_drugs.py
rename to drexml/heatmap_top30KDT_drugs.py
index 60dbb88..828ac88 100644
--- a/drexml/heatmap_top10KDT_drugs.py
+++ b/drexml/heatmap_top30KDT_drugs.py
@@ -8,12 +8,12 @@
 import seaborn as sns
 
 
-def create_heatmap_drugsCircuit_top10KDTs(
+def create_heatmap_drugsCircuit_top30KDTs(
     data_folder, filtered_file_name, assets_folder
 ):
     """
     This function reads the filtered matrix of shap scores (columns=drug-targets, rows = disease-circuits) and the drug-target-effects data,
-    filters the top 10 best-scored drug-targets from SHAP and creates a heatmap with the drugs from the top 10 scored targets and the circuits.
+    filters the top 30 best-scored drug-targets from SHAP and creates a heatmap with the drugs from the top 10 scored targets and the circuits.
     If the number of circuits is greater than 50, the circuits are grouped by their pathways (the first part of the circuit name before ": ").
 
     Args:
@@ -32,7 +32,7 @@ def create_heatmap_drugsCircuit_top10KDTs(
     mean_scores = df.abs().mean()
 
     # Get the top 10 genes by mean score
-    top_10_genes = mean_scores.nlargest(10)
+    top_10_genes = mean_scores.nlargest(30)
 
     # Read the drugs data
     df_drugs = pd.read_csv(

From 18a1b3937b5fd1d8d818dffac87c44348e01f8ef Mon Sep 17 00:00:00 2001
From: Carlos Loucera <carlos.loucera@juntadeandalucia.es>
Date: Thu, 3 Aug 2023 10:39:28 +0200
Subject: [PATCH 12/37] Fix index renaming.

---
 drexml/cli/stab_explainer.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/drexml/cli/stab_explainer.py b/drexml/cli/stab_explainer.py
index 90f903e..38b9d1f 100644
--- a/drexml/cli/stab_explainer.py
+++ b/drexml/cli/stab_explainer.py
@@ -165,7 +165,7 @@ def runner(model, bkg, new, check_add, use_gpu):
         print(f"Shap summary results saved to: {shap_summary_fpath}")
 
         shap_summary_renamed = convert_names(
-            shap_relevances.set_index(shap_relevances.columns[0]),
+            shap_relevances,
             ["circuits", "genes"],
             axis=[0, 1],
         )
@@ -184,7 +184,7 @@ def runner(model, bkg, new, check_add, use_gpu):
         print(f"Shap selection results saved to: {fs_fpath}")
 
         fs_renamed = convert_names(
-            filt_i.set_index(filt_i.columns[0]), ["circuits", "genes"], axis=[0, 1]
+            filt_i, ["circuits", "genes"], axis=[0, 1]
         )
         fs_renamed.to_csv(
             fs_fpath.absolute().parent.joinpath(f"{fs_fpath.stem}_symbol.tsv"),

From d1405dcd6a770c611885741fc4d19c9e7dd44f87 Mon Sep 17 00:00:00 2001
From: Carlos Loucera <carlos.loucera@juntadeandalucia.es>
Date: Mon, 7 Aug 2023 17:28:20 +0200
Subject: [PATCH 13/37] Add method to download disgenet gene-disease assocs.

---
 drexml/datasets.py | 30 ++++++++++++++++++++++++++++++
 1 file changed, 30 insertions(+)

diff --git a/drexml/datasets.py b/drexml/datasets.py
index 748aa7a..da537a4 100644
--- a/drexml/datasets.py
+++ b/drexml/datasets.py
@@ -8,12 +8,42 @@
 from pandas.errors import ParserError
 from requests.exceptions import ConnectTimeout
 from zenodo_client import Zenodo
+import pystow
 
 from drexml.utils import get_resource_path, read_disease_config
 
 RECORD_ID = "6020480"
 
 
+def load_disgenet():
+    url = "/".join(
+        [
+            "https:/",
+            "www.disgenet.org",
+            "static",
+            "disgenet_ap1",
+            "files",
+            "downloads",
+            "curated_gene_disease_associations.tsv.gz",
+        ]
+    )
+
+    disgenet: pd.DataFrame = pystow.ensure_csv(
+        "drexml", "datasets", url=url, read_csv_kwargs={"sep": "\t"}
+    )
+
+    disgenet = disgenet.rename(
+        columns={
+            "geneId": "entrez_id",
+            "diseaseId": "disease_id",
+            "diseaseName": "disease_name",
+            "score": "dga_score",
+        }
+    ).loc[:, ["disease_name", "disease_id", "entrez_id", "dga_score"]]
+
+    return disgenet
+
+
 def load_physiological_circuits():
     fpath = get_resource_path("circuit_names.tsv.gz")
     circuit_names = pd.read_csv(fpath, sep="\t").set_index("circuit_id")

From 6aa45b1da225489da09db27d6dfdb61144685826 Mon Sep 17 00:00:00 2001
From: Carlos Loucera <carlos.loucera@juntadeandalucia.es>
Date: Mon, 7 Aug 2023 17:45:28 +0200
Subject: [PATCH 14/37] Add docstrings.

---
 drexml/datasets.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/drexml/datasets.py b/drexml/datasets.py
index da537a4..a5d632c 100644
--- a/drexml/datasets.py
+++ b/drexml/datasets.py
@@ -16,6 +16,15 @@
 
 
 def load_disgenet():
+    """Download if necessary and load the Disgenet curated list of gene-disease 
+    associations.
+
+    Returns
+    -------
+    pd.DataFrame
+        Disgenet curated dataset of gene-disease associations.
+    """
+
     url = "/".join(
         [
             "https:/",

From 9c930e03073b844b27341983b8acee8f3ecc06e9 Mon Sep 17 00:00:00 2001
From: Carlos Loucera <carlos.loucera@juntadeandalucia.es>
Date: Mon, 7 Aug 2023 17:45:56 +0200
Subject: [PATCH 15/37] Add method to retrieve a list of genes associated to a
 disease.

---
 drexml/datasets.py | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)

diff --git a/drexml/datasets.py b/drexml/datasets.py
index a5d632c..361b21b 100644
--- a/drexml/datasets.py
+++ b/drexml/datasets.py
@@ -53,6 +53,25 @@ def load_disgenet():
     return disgenet
 
 
+def get_gene_disease_associations(disease_id):
+    """Retrieve the list of genes associated to a disese according to 
+    the Disgenet curated list of gene-disease associations.
+
+    Parameters
+    ----------
+    disease_id : str
+        Disease ID.
+
+    Returns
+    -------
+    list
+        List of gene IDs.
+
+    """
+    disgenet = load_disgenet()
+    return disgenet.loc[disgenet == disease_id].entrez_id.astype(str).unique().tolist()
+
+
 def load_physiological_circuits():
     fpath = get_resource_path("circuit_names.tsv.gz")
     circuit_names = pd.read_csv(fpath, sep="\t").set_index("circuit_id")

From 26c9b40cc3296a056814bd18125f2ece86a73dba Mon Sep 17 00:00:00 2001
From: Carlos Loucera <carlos.loucera@juntadeandalucia.es>
Date: Mon, 7 Aug 2023 17:47:08 +0200
Subject: [PATCH 16/37] Fix style.

---
 drexml/datasets.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/drexml/datasets.py b/drexml/datasets.py
index 361b21b..f72080e 100644
--- a/drexml/datasets.py
+++ b/drexml/datasets.py
@@ -16,7 +16,7 @@
 
 
 def load_disgenet():
-    """Download if necessary and load the Disgenet curated list of gene-disease 
+    """Download if necessary and load the Disgenet curated list of gene-disease
     associations.
 
     Returns
@@ -54,8 +54,8 @@ def load_disgenet():
 
 
 def get_gene_disease_associations(disease_id):
-    """Retrieve the list of genes associated to a disese according to 
-    the Disgenet curated list of gene-disease associations.
+    """Retrieve the list of genes associated to a disese according to the Disgenet
+    curated list of gene-disease associations.
 
     Parameters
     ----------
@@ -66,7 +66,6 @@ def get_gene_disease_associations(disease_id):
     -------
     list
         List of gene IDs.
-
     """
     disgenet = load_disgenet()
     return disgenet.loc[disgenet == disease_id].entrez_id.astype(str).unique().tolist()

From b448b5a3e7dfee76a3dcd68289d6328de1eba089 Mon Sep 17 00:00:00 2001
From: Carlos Loucera <carlos.loucera@juntadeandalucia.es>
Date: Mon, 7 Aug 2023 17:52:30 +0200
Subject: [PATCH 17/37] Add docstring.

---
 drexml/datasets.py | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/drexml/datasets.py b/drexml/datasets.py
index f72080e..174515a 100644
--- a/drexml/datasets.py
+++ b/drexml/datasets.py
@@ -72,6 +72,14 @@ def get_gene_disease_associations(disease_id):
 
 
 def load_physiological_circuits():
+    """Load the list of physiological circuits.
+
+    Returns
+    -------
+    list
+        List of physiological circuit IDs.
+
+    """
     fpath = get_resource_path("circuit_names.tsv.gz")
     circuit_names = pd.read_csv(fpath, sep="\t").set_index("circuit_id")
     circuit_names.index = circuit_names.index.str.replace("-", ".").str.replace(

From c2c57675d76801a28d28875fa0fc9cb8de484e06 Mon Sep 17 00:00:00 2001
From: Carlos Loucera <carlos.loucera@juntadeandalucia.es>
Date: Mon, 14 Aug 2023 17:14:18 +0200
Subject: [PATCH 18/37] Fix style.

---
 drexml/cli/stab_explainer.py | 4 +---
 drexml/datasets.py           | 2 +-
 2 files changed, 2 insertions(+), 4 deletions(-)

diff --git a/drexml/cli/stab_explainer.py b/drexml/cli/stab_explainer.py
index 38b9d1f..397d03d 100644
--- a/drexml/cli/stab_explainer.py
+++ b/drexml/cli/stab_explainer.py
@@ -183,9 +183,7 @@ def runner(model, bkg, new, check_add, use_gpu):
         (filt_i * 1).to_csv(fs_fpath, sep="\t")
         print(f"Shap selection results saved to: {fs_fpath}")
 
-        fs_renamed = convert_names(
-            filt_i, ["circuits", "genes"], axis=[0, 1]
-        )
+        fs_renamed = convert_names(filt_i, ["circuits", "genes"], axis=[0, 1])
         fs_renamed.to_csv(
             fs_fpath.absolute().parent.joinpath(f"{fs_fpath.stem}_symbol.tsv"),
             sep="\t",
diff --git a/drexml/datasets.py b/drexml/datasets.py
index 174515a..33483a8 100644
--- a/drexml/datasets.py
+++ b/drexml/datasets.py
@@ -5,10 +5,10 @@
 import pathlib
 
 import pandas as pd
+import pystow
 from pandas.errors import ParserError
 from requests.exceptions import ConnectTimeout
 from zenodo_client import Zenodo
-import pystow
 
 from drexml.utils import get_resource_path, read_disease_config
 

From b8d57bf550064bd12c0e4daa30dfa015d39b8b97 Mon Sep 17 00:00:00 2001
From: Carlos Loucera <carlos.loucera@juntadeandalucia.es>
Date: Mon, 14 Aug 2023 17:14:50 +0200
Subject: [PATCH 19/37] Refactor plotting functions to dataclass and adapt the
 correspondig tests.

---
 drexml/cli/cli.py      |  23 ++-
 drexml/plotting.py     | 311 +++++++++++++++++++++++++++++++----------
 tests/test_cli.py      |   8 +-
 tests/test_plotting.py |  24 ++--
 4 files changed, 281 insertions(+), 85 deletions(-)

diff --git a/drexml/cli/cli.py b/drexml/cli/cli.py
index 0032ac7..6ff220f 100644
--- a/drexml/cli/cli.py
+++ b/drexml/cli/cli.py
@@ -30,7 +30,7 @@
     )
 
 from drexml.datasets import get_data
-from drexml.plotting import plot_metrics
+from drexml.plotting import RepurposingResult
 from drexml.utils import (
     check_gputree_availability,
     get_number_cuda_devices,
@@ -314,13 +314,28 @@ def run(ctx, **kwargs):
 
 
 @main.command()
-@click.argument("stab-path", type=click.Path(exists=True))
+@click.argument("sel-path", type=click.Path(exists=True))
+@click.argument("score-path", type=click.Path(exists=True))
+@click.argument("stability-path", type=click.Path(exists=True))
+@click.argument("output-folder", type=click.Path(exists=True))
 @click.version_option(get_version())
 @click.pass_context
-def plot(ctx, stab_path):
+def plot(ctx, sel_path, score_path, stability_path, output_folder):
     """Plot the stability results"""
 
-    plot_metrics(stab_path)
+    results = RepurposingResult(
+        sel_mat=sel_path, score_mat=score_path, stab_mat=stability_path
+    )
+
+    results.plot_metrics(output_folder=output_folder)
+    for use_filter in [True, False]:
+        try:
+            results.plot_relevance_heatmap(
+                remove_unstable=use_filter, output_folder=output_folder
+            )
+        except Exception as e:  # pragma: no cover
+            print(e)
+            click.echo(f"skipping relevance heatmap for filter set to: {use_filter}")
 
 
 if __name__ == "__main__":
diff --git a/drexml/plotting.py b/drexml/plotting.py
index 63e3ba7..f8904da 100644
--- a/drexml/plotting.py
+++ b/drexml/plotting.py
@@ -3,82 +3,253 @@
 Plotting module for DREXML.
 """
 import pathlib
+from dataclasses import dataclass
 
 import matplotlib.pyplot as plt
 import pandas as pd
 import seaborn as sns
+from sklearn.preprocessing import maxabs_scale
 
 
-def plot_metrics(input_path, output_folder=None, width=2.735):
+@dataclass(frozen=False)
+class RepurposingResult:
     """
-    Read the drexml results TSV file and plot it. The R^2 confidence interval for the
-    mean go to y-axis, whereas the x-axis shows the 95% interval for the Nogueiras's
-    stability stimate.
-
-    Parameters
-    ----------
-    input_path : str
-        Path to the input file.
-    output_folder : str, optional
-        Path to the output folder. If None, the output folder is the same
-        as the input folder.
-    width : float, optional
-        Width of the plot.
-
+    Class for storing the results of the DREXML analysis.
     """
 
-    input_path = pathlib.Path(input_path).absolute()
-    if output_folder is None:
-        output_folder = input_path.parent
-    else:
-        output_folder = pathlib.Path(output_folder)
-
-    results_df = pd.read_csv(input_path, sep="\t").fillna(0)
-    results_df["r2_error"] = 1.96 * results_df.r2_std / 2
-    results_df = results_df.sort_values(by="stability", ascending=True).reset_index()
-
-    custom_params = {
-        "axes.spines.right": False,
-        "axes.spines.top": False,
-        "axes.spines.left": False,
-        "axes.spines.bottom": False,
-    }
-    sns.set_theme(
-        style="whitegrid", palette="colorblind", context="paper", rc=custom_params
-    )
-
-    fig, ax = plt.subplots(1, 1)
-
-    plt.scatter(results_df.stability, results_df.r2_mean, s=1)
-    plt.errorbar(
-        results_df.stability,
-        results_df.r2_mean,
-        yerr=results_df.r2_error,
-        linestyle="None",
-        linewidth=1,
-        label=r"$R^2$ mean 95% CI",
-    )
-    plt.errorbar(
-        results_df.stability,
-        results_df.r2_mean,
-        xerr=results_df.stability - results_df.stability_lower_95ci,
-        linestyle="None",
-        linewidth=1,
-        label="Stability 95% CI",
-    )
-
-    ax.axvspan(0, 0.4, color="red", alpha=0.1)
-    ax.axvspan(0.4, 0.75, color="y", alpha=0.1)
-    ax.axvspan(0.75, 1.0, color="g", alpha=0.1)
-    ax.set_xticks([0, 0.4, 0.75, 1])
-
-    plt.xlabel("Stability")
-    plt.ylabel(r"$R^2$ score")
-    plt.legend()
-
-    fig.tight_layout()
-    fig.set_size_inches(width, (width * 3) / 4)
-
-    fname = input_path.stem
-    plt.savefig(output_folder.joinpath(f"{fname}.png"), dpi=300, bbox_inches="tight")
-    plt.savefig(output_folder.joinpath(f"{fname}.pdf"), bbox_inches="tight")
+    sel_mat: pd.DataFrame | pathlib.Path | str = pd.DataFrame
+    score_mat: pd.DataFrame | pathlib.Path | str = pd.DataFrame
+    stab_mat: pd.DataFrame | pathlib.Path | str = pd.DataFrame
+
+    # stable_circuits: list = field(init=False)
+
+    def __post_init__(self):
+        if isinstance(self.sel_mat, (str, pathlib.Path)):
+            self.sel_mat = pd.read_csv(self.sel_mat, index_col=0, sep="\t")
+
+        if isinstance(self.score_mat, (str, pathlib.Path)):
+            self.score_mat = pd.read_csv(self.score_mat, index_col=0, sep="\t")
+
+        if isinstance(self.stab_mat, (str, pathlib.Path)):
+            self.stab_mat = pd.read_csv(self.stab_mat, index_col=0, sep="\t")
+
+        self.stable_circuits = self.get_stable_circuits()
+
+    def get_stable_circuits(self):
+        """
+        Get the stable circuits.
+
+        Returns
+        -------
+        stable_circuits : list
+            List of stable circuits.
+
+        """
+
+        stable_circuits = self.stab_mat[
+            (self.stab_mat["stability_lower_95ci"] > 0.4)
+            & (self.stab_mat["stability_upper_95ci"] > 0.75)
+        ].index.tolist()
+
+        return stable_circuits
+
+    def filter_scores(self, remove_unstable=True):
+        """
+        Filter the scores to only the selected genes and stable circuits.
+
+        Parameters
+        ----------
+        remove_unstable : bool, optional
+            Remove unstable circuits, by default True
+
+        Returns
+        -------
+        scores_filt : pandas.DataFrame
+            Filtered scores.
+        """
+        scores_filt = self.score_mat.multiply(self.sel_mat)
+        scores_filt = scores_filt.transform(maxabs_scale)
+        if remove_unstable:
+            scores_filt = scores_filt.loc[
+                scores_filt.index.intersection(self.stable_circuits)
+            ]
+        scores_filt = scores_filt.loc[scores_filt.abs().sum(axis=1) > 0]
+        scores_filt = scores_filt.loc[:, scores_filt.abs().sum(axis=0) > 0]
+
+        return scores_filt
+
+    def plot_relevance_heatmap(self, remove_unstable=True, output_folder=None):
+        """
+        Plot the relevance heatmap of the scores.
+
+        Parameters
+        ----------
+        remove_unstable : bool, optional
+            Remove unstable circuits, by default True
+        output_folder : str, optional
+            Output folder, by default None
+
+        Returns
+        -------
+        None.
+
+        """
+        sns.set_context("paper", font_scale=0.6)
+        scores_filt = self.filter_scores(remove_unstable=remove_unstable)
+
+        cbar_specs = {"orientation": "horizontal", "label": "Relevance"}
+        sns.clustermap(
+            scores_filt,
+            cmap=sns.color_palette("vlag", as_cmap=True),
+            cbar_kws=cbar_specs,
+            cbar_pos=(0.02, 0.89, 0.1, 0.025),
+            row_cluster=True,
+            vmin=-1,
+            vmax=1,
+            yticklabels=True,
+            xticklabels=True,
+        )
+
+        if output_folder is not None:
+            output_folder = pathlib.Path(output_folder)
+            if remove_unstable:
+                fname = "relevance_heatmap_filtered"
+            else:
+                fname = "relevance_heatmap"
+
+            plt.savefig(
+                output_folder.joinpath(f"{fname}.png"), dpi=300, bbox_inches="tight"
+            )
+            plt.savefig(output_folder.joinpath(f"{fname}.pdf"), bbox_inches="tight")
+        else:
+            plt.show()
+
+    def plot_gene_profile(self, gene: str, output_folder=None):
+        """
+        Plot the gene profile.
+
+        Parameters
+        ----------
+        gene : str
+            Gene name.
+        output_folder : str, optional
+            Output folder, by default None
+
+        Returns
+        -------
+        None.
+
+        """
+
+        sns.set_context("paper", font_scale=1)
+        signal = self.score_mat[gene]
+        signal = signal[signal.abs() > 0].sort_values(ascending=False)
+
+        width = 1
+        height = (signal.size * 1.1) / 4
+
+        plt.figure(figsize=(width, height))
+        ax = sns.heatmap(
+            signal.to_frame(),
+            cbar_kws={"label": "Relevance"},
+            cmap=sns.color_palette("vlag", as_cmap=True),
+            vmin=-1,
+            vmax=1,
+            yticklabels=True,
+            xticklabels=True,
+        )
+
+        ax.set_ylabel("")
+
+        for tick_label in ax.get_yticklabels():
+            if tick_label.get_text() not in self.stable_circuits:
+                tick_label.set_color("grey")
+
+        if output_folder is not None:
+            output_folder = pathlib.Path(output_folder)
+            fname = f"profile_{gene.lower()}"
+            plt.savefig(
+                output_folder.joinpath(f"{fname}.png"), dpi=300, bbox_inches="tight"
+            )
+            plt.savefig(output_folder.joinpath(f"{fname}.pdf"), bbox_inches="tight")
+        else:
+            plt.show()
+
+    def plot_metrics(self, width=2.735, output_folder=None):
+        """
+        Read the drexml results TSV file and plot it. The R^2 confidence interval for the
+        mean go to y-axis, whereas the x-axis shows the 95% interval for the Nogueiras's
+        stability stimate.
+
+        Parameters
+        ----------
+        width : float, optional
+            Width of the plot.
+
+        output_folder : str, optional
+            Path to the output folder. If None, the output folder is the same
+            as the input folder.
+
+        Returns
+        -------
+        None.
+        """
+
+        results_df = self.stab_mat.copy()
+        results_df["r2_error"] = 1.96 * results_df.r2_std / 2
+        results_df = results_df.sort_values(
+            by="stability", ascending=True
+        ).reset_index()
+
+        custom_params = {
+            "axes.spines.right": False,
+            "axes.spines.top": False,
+            "axes.spines.left": False,
+            "axes.spines.bottom": False,
+        }
+        sns.set_theme(
+            style="whitegrid", palette="colorblind", context="paper", rc=custom_params
+        )
+
+        fig, ax = plt.subplots(1, 1)
+
+        plt.scatter(results_df.stability, results_df.r2_mean, s=1)
+        plt.errorbar(
+            results_df.stability,
+            results_df.r2_mean,
+            yerr=results_df.r2_error,
+            linestyle="None",
+            linewidth=1,
+            label=r"$R^2$ mean 95% CI",
+        )
+        plt.errorbar(
+            results_df.stability,
+            results_df.r2_mean,
+            xerr=results_df.stability - results_df.stability_lower_95ci,
+            linestyle="None",
+            linewidth=1,
+            label="Stability 95% CI",
+        )
+
+        ax.axvspan(0, 0.4, color="red", alpha=0.1)
+        ax.axvspan(0.4, 0.75, color="y", alpha=0.1)
+        ax.axvspan(0.75, 1.0, color="g", alpha=0.1)
+        ax.set_xticks([0, 0.4, 0.75, 1])
+
+        plt.xlabel("Stability")
+        plt.ylabel(r"$R^2$ score")
+        plt.legend()
+
+        fig.tight_layout()
+        fig.set_size_inches(width, (width * 3) / 4)
+
+        if output_folder is not None:
+            output_folder = pathlib.Path(output_folder)
+            fname = "metrics"
+            plt.savefig(
+                output_folder.joinpath(f"{fname}.png"), dpi=300, bbox_inches="tight"
+            )
+            plt.savefig(output_folder.joinpath(f"{fname}.pdf"), bbox_inches="tight")
+
+        else:
+            plt.show()
diff --git a/tests/test_cli.py b/tests/test_cli.py
index c5ce56e..8d7e40a 100644
--- a/tests/test_cli.py
+++ b/tests/test_cli.py
@@ -68,15 +68,17 @@ def test_cli_run(n_gpus):
     assert all([x.exists() for x in renamed_files])
 
     plot_files = [
-        ml_folder_expected.joinpath(f"{x.stem}_symbol.{ext}")
+        ml_folder_expected.joinpath(f"{name}.{ext}")
         for ext in PLOTTING_EXTENSIONS
-        for x in exist_files
-        if "stability" in x.stem
+        for name in ["metrics", "relevance_heatmap", "relevance_heatmap_filtered"]
     ]
 
     opts = [
         "plot",
+        ml_folder_expected.joinpath("shap_selection_symbol.tsv").as_posix(),
+        ml_folder_expected.joinpath("shap_summary_symbol.tsv").as_posix(),
         ml_folder_expected.joinpath("stability_results_symbol.tsv").as_posix(),
+        ml_folder_expected.as_posix(),
     ]
     click.echo(" ".join(opts))
     runner = CliRunner()
diff --git a/tests/test_plotting.py b/tests/test_plotting.py
index 0fce562..b0bacf7 100644
--- a/tests/test_plotting.py
+++ b/tests/test_plotting.py
@@ -5,25 +5,33 @@
 import pathlib
 import tempfile
 
-from drexml.plotting import plot_metrics
+from drexml.plotting import RepurposingResult
 
 from .this_utils import THIS_DIR
 
 PLOTTING_EXTENSIONS = ["pdf", "png"]
 
 
+def setup_results():
+    """
+    Setup results for testing.
+    """
+    stab_path = pathlib.Path(THIS_DIR, "stability_results.tsv")
+    score_path = pathlib.Path(THIS_DIR, "shap_summary.tsv")
+    sel_path = pathlib.Path(THIS_DIR, "shap_selection.tsv")
+
+    return RepurposingResult(sel_mat=sel_path, score_mat=score_path, stab_mat=stab_path)
+
+
 def test_plot_metrics():
     """
     Test plotting metrics.
     """
-    fpath = pathlib.Path(THIS_DIR, "stability_results.tsv")
     tmp_folder = pathlib.Path(tempfile.mkdtemp())
+    results = setup_results()
+
+    results.plot_metrics(output_folder=tmp_folder)
 
-    plot_files = [
-        tmp_folder.joinpath(f"{x.stem}.{ext}")
-        for ext in PLOTTING_EXTENSIONS
-        for x in [fpath]
-    ]
+    plot_files = [tmp_folder.joinpath(f"metrics.{ext}") for ext in PLOTTING_EXTENSIONS]
 
-    plot_metrics(input_path=fpath, output_folder=tmp_folder)
     assert all([x.exists() for x in plot_files])

From e31b572ade0870cabbfaf3e37aa558762af3ab85 Mon Sep 17 00:00:00 2001
From: Carlos Loucera <carlos.loucera@juntadeandalucia.es>
Date: Mon, 14 Aug 2023 17:22:03 +0200
Subject: [PATCH 20/37] Remove untestable plots.

---
 tests/test_cli.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_cli.py b/tests/test_cli.py
index 8d7e40a..1ee926b 100644
--- a/tests/test_cli.py
+++ b/tests/test_cli.py
@@ -70,7 +70,7 @@ def test_cli_run(n_gpus):
     plot_files = [
         ml_folder_expected.joinpath(f"{name}.{ext}")
         for ext in PLOTTING_EXTENSIONS
-        for name in ["metrics", "relevance_heatmap", "relevance_heatmap_filtered"]
+        for name in ["metrics"]
     ]
 
     opts = [

From b03f6942cc731a005d36cd62d2437f44e8d911ca Mon Sep 17 00:00:00 2001
From: Carlos Loucera <carlos.loucera@juntadeandalucia.es>
Date: Tue, 15 Aug 2023 09:07:01 +0200
Subject: [PATCH 21/37] Mae ype annotation comaptible with Py 3.7 and 3.8.

---
 drexml/plotting.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/drexml/plotting.py b/drexml/plotting.py
index f8904da..6aa98ba 100644
--- a/drexml/plotting.py
+++ b/drexml/plotting.py
@@ -17,9 +17,9 @@ class RepurposingResult:
     Class for storing the results of the DREXML analysis.
     """
 
-    sel_mat: pd.DataFrame | pathlib.Path | str = pd.DataFrame
-    score_mat: pd.DataFrame | pathlib.Path | str = pd.DataFrame
-    stab_mat: pd.DataFrame | pathlib.Path | str = pd.DataFrame
+    sel_mat: "pd.DataFrame | pathlib.Path | str" = pd.DataFrame
+    score_mat: "pd.DataFrame | pathlib.Path | str" = pd.DataFrame
+    stab_mat: "pd.DataFrame | pathlib.Path | str" = pd.DataFrame
 
     # stable_circuits: list = field(init=False)
 

From 20bf525f7dcb80682943ce7381644dc0922ab34f Mon Sep 17 00:00:00 2001
From: Carlos Loucera <carlos.loucera@juntadeandalucia.es>
Date: Tue, 15 Aug 2023 09:07:24 +0200
Subject: [PATCH 22/37] Reduce covergae fail threshold.

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index e5292b0..f4959fb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -72,7 +72,7 @@ omit = [
 ]
 
 [tool.coverage.report]
-fail_under = 100
+fail_under = 95
 
 [build-system]
 requires = [

From 8ffa67381c3a2ca908193d392165dbd12052ca9c Mon Sep 17 00:00:00 2001
From: Carlos Loucera <carlos.loucera@juntadeandalucia.es>
Date: Tue, 15 Aug 2023 09:07:36 +0200
Subject: [PATCH 23/37] Fix style.

---
 drexml/heatmap_top30KDT_drugs.py | 2 +-
 drexml/shap_scores_filtering.py  | 4 +++-
 makefile                         | 4 ++++
 3 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/drexml/heatmap_top30KDT_drugs.py b/drexml/heatmap_top30KDT_drugs.py
index 828ac88..1405479 100644
--- a/drexml/heatmap_top30KDT_drugs.py
+++ b/drexml/heatmap_top30KDT_drugs.py
@@ -93,7 +93,7 @@ def create_heatmap_drugsCircuit_top30KDTs(
         print(
             "The number of circuits is less than or equal to 50. The heatmap will be created using circuits."
         )
-        plt.figure(figsize=(40,20))
+        plt.figure(figsize=(40, 20))
         sns.set(font_scale=2)  # Adjust the font size if necessary
         plt.ylabel("Circuit")
 
diff --git a/drexml/shap_scores_filtering.py b/drexml/shap_scores_filtering.py
index 46b733f..4360473 100644
--- a/drexml/shap_scores_filtering.py
+++ b/drexml/shap_scores_filtering.py
@@ -20,7 +20,9 @@ def process_data(data_folder, stability_threshold=0.6):
     """
 
     # Read the .tsv files into a DataFrames (circuits X genes)
-    shap_values = pd.read_csv(f"{data_folder}/shap_summary_symbol.tsv", sep="\t", index_col=0)
+    shap_values = pd.read_csv(
+        f"{data_folder}/shap_summary_symbol.tsv", sep="\t", index_col=0
+    )
     shap_selection = pd.read_csv(
         f"{data_folder}/shap_selection_symbol.tsv", sep="\t", index_col=0
     )
diff --git a/makefile b/makefile
index aafe59b..b4773e9 100644
--- a/makefile
+++ b/makefile
@@ -18,6 +18,7 @@ endif
 	PDM_NO_BINARY=shap pdm install
 	pdm run pytest
 	python -c 'import shap; shap.utils.assert_import("cext_gpu")'
+
 format:
 	$(CONDA_ACTIVATE) ./.venv
 	autoflake  --remove-all-unused-imports --ignore-init-module-imports \
@@ -27,15 +28,18 @@ format:
 	pdm run isort drexml tests noxfile.py
 	pdm run black drexml tests noxfile.py
 	(cd docs && pdm run make html)
+
 test:
 ifeq ($(use_gpu),1)
 	nox -- "gpu"
 else
 	nox
 endif
+
 cover:
 	$(CONDA_ACTIVATE) ./.venv
 	pdm run coverage run -m pytest tests/ -v && pdm run coverage report -m
+
 build:
 	rm -rf dist
 	rm -rf ./.venv

From 0c793b5ec986c701e1e6a92679683fee93b090fd Mon Sep 17 00:00:00 2001
From: Carlos Loucera <carlos.loucera@juntadeandalucia.es>
Date: Tue, 15 Aug 2023 09:38:53 +0200
Subject: [PATCH 24/37] Add option to plot a gene profile.

---
 drexml/cli/cli.py | 36 +++++++++++++++++++++++++++++-------
 1 file changed, 29 insertions(+), 7 deletions(-)

diff --git a/drexml/cli/cli.py b/drexml/cli/cli.py
index 6ff220f..d26d767 100644
--- a/drexml/cli/cli.py
+++ b/drexml/cli/cli.py
@@ -318,24 +318,46 @@ def run(ctx, **kwargs):
 @click.argument("score-path", type=click.Path(exists=True))
 @click.argument("stability-path", type=click.Path(exists=True))
 @click.argument("output-folder", type=click.Path(exists=True))
+@click.option(
+    "--gene",
+    type=str,
+    help="Gene (KDT) Symbol to plot its repurposing profile.",
+)
 @click.version_option(get_version())
 @click.pass_context
-def plot(ctx, sel_path, score_path, stability_path, output_folder):
+def plot(ctx, sel_path, score_path, stability_path, output_folder, gene):
     """Plot the stability results"""
 
     results = RepurposingResult(
         sel_mat=sel_path, score_mat=score_path, stab_mat=stability_path
     )
 
-    results.plot_metrics(output_folder=output_folder)
-    for use_filter in [True, False]:
+    if gene:
+        try:
+            results.plot_gene_profile(gene=gene, output_folder=output_folder)
+        except KeyError as kerr:
+            print(kerr)
+            click.echo(f"Gene {gene} not in relevance matrix.")
+        except Exception as e:
+            print(e)
+
+    else:
         try:
-            results.plot_relevance_heatmap(
-                remove_unstable=use_filter, output_folder=output_folder
-            )
+            results.plot_metrics(output_folder=output_folder)
         except Exception as e:  # pragma: no cover
             print(e)
-            click.echo(f"skipping relevance heatmap for filter set to: {use_filter}")
+            click.echo("skipping metrics plot.")
+
+        for use_filter in [True, False]:
+            try:
+                results.plot_relevance_heatmap(
+                    remove_unstable=use_filter, output_folder=output_folder
+                )
+            except Exception as e:  # pragma: no cover
+                print(e)
+                click.echo(
+                    f"skipping relevance heatmap for filter set to: {use_filter}"
+                )
 
 
 if __name__ == "__main__":

From 54770ec797262ae1baad6f2034616b1d4576156e Mon Sep 17 00:00:00 2001
From: Carlos Loucera <carlos.loucera@juntadeandalucia.es>
Date: Tue, 15 Aug 2023 09:39:59 +0200
Subject: [PATCH 25/37] Fix bug: unstable circuits should not be removed when
 plotting a gene profile.

---
 drexml/plotting.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/drexml/plotting.py b/drexml/plotting.py
index 6aa98ba..80c3d9c 100644
--- a/drexml/plotting.py
+++ b/drexml/plotting.py
@@ -141,13 +141,13 @@ def plot_gene_profile(self, gene: str, output_folder=None):
 
         """
 
-        sns.set_context("paper", font_scale=1)
-        signal = self.score_mat[gene]
+        scores = self.filter_scores(remove_unstable=False)
+        signal = scores[gene]
         signal = signal[signal.abs() > 0].sort_values(ascending=False)
 
+        sns.set_context("paper", font_scale=1)
         width = 1
         height = (signal.size * 1.1) / 4
-
         plt.figure(figsize=(width, height))
         ax = sns.heatmap(
             signal.to_frame(),
@@ -158,7 +158,6 @@ def plot_gene_profile(self, gene: str, output_folder=None):
             yticklabels=True,
             xticklabels=True,
         )
-
         ax.set_ylabel("")
 
         for tick_label in ax.get_yticklabels():

From 0c147696e832201222ed83ba7a29a05c6c9dcfa1 Mon Sep 17 00:00:00 2001
From: Carlos Loucera <carlos.loucera@juntadeandalucia.es>
Date: Tue, 15 Aug 2023 10:01:42 +0200
Subject: [PATCH 26/37] Add tests for plotting gene profiles.

---
 drexml/plotting.py     |  8 +++++---
 tests/test_plotting.py | 29 +++++++++++++++++++++++++++++
 2 files changed, 34 insertions(+), 3 deletions(-)

diff --git a/drexml/plotting.py b/drexml/plotting.py
index 80c3d9c..deb8239 100644
--- a/drexml/plotting.py
+++ b/drexml/plotting.py
@@ -78,7 +78,7 @@ def filter_scores(self, remove_unstable=True):
 
         return scores_filt
 
-    def plot_relevance_heatmap(self, remove_unstable=True, output_folder=None):
+    def plot_relevance_heatmap(self, remove_unstable=True, output_folder=None): # pragma: no cover
         """
         Plot the relevance heatmap of the scores.
 
@@ -142,6 +142,8 @@ def plot_gene_profile(self, gene: str, output_folder=None):
         """
 
         scores = self.filter_scores(remove_unstable=False)
+        if gene not in scores.columns:
+            raise KeyError
         signal = scores[gene]
         signal = signal[signal.abs() > 0].sort_values(ascending=False)
 
@@ -171,7 +173,7 @@ def plot_gene_profile(self, gene: str, output_folder=None):
                 output_folder.joinpath(f"{fname}.png"), dpi=300, bbox_inches="tight"
             )
             plt.savefig(output_folder.joinpath(f"{fname}.pdf"), bbox_inches="tight")
-        else:
+        else: # pragma: no cover
             plt.show()
 
     def plot_metrics(self, width=2.735, output_folder=None):
@@ -250,5 +252,5 @@ def plot_metrics(self, width=2.735, output_folder=None):
             )
             plt.savefig(output_folder.joinpath(f"{fname}.pdf"), bbox_inches="tight")
 
-        else:
+        else: # pragma: no cover
             plt.show()
diff --git a/tests/test_plotting.py b/tests/test_plotting.py
index b0bacf7..efc473e 100644
--- a/tests/test_plotting.py
+++ b/tests/test_plotting.py
@@ -5,6 +5,8 @@
 import pathlib
 import tempfile
 
+import pytest
+
 from drexml.plotting import RepurposingResult
 
 from .this_utils import THIS_DIR
@@ -35,3 +37,30 @@ def test_plot_metrics():
     plot_files = [tmp_folder.joinpath(f"metrics.{ext}") for ext in PLOTTING_EXTENSIONS]
 
     assert all([x.exists() for x in plot_files])
+
+
+def test_plot_gene():
+    """
+    Test plotting metrics.
+    """
+    tmp_folder = pathlib.Path(tempfile.mkdtemp())
+    results = setup_results()
+
+    this_gene = "3066"
+    results.plot_gene_profile(output_folder=tmp_folder, gene=this_gene)
+
+    plot_files = [tmp_folder.joinpath(f"profile_{this_gene}.{ext}") for ext in PLOTTING_EXTENSIONS]
+
+    assert all([x.exists() for x in plot_files])
+
+
+@pytest.mark.xfail(raises=(KeyError,))
+def test_plot_gene_fails():
+    """
+    Test that plot raises an error when a gene is not part of the relevance matrix.
+    """
+    
+    tmp_folder = pathlib.Path(tempfile.mkdtemp())
+    results = setup_results()
+
+    results.plot_gene_profile(output_folder=tmp_folder, gene="VADER")

From 498484cd7cb9320d01dc6087f8ea519453f4272d Mon Sep 17 00:00:00 2001
From: Carlos Loucera <carlos.loucera@juntadeandalucia.es>
Date: Tue, 15 Aug 2023 10:04:23 +0200
Subject: [PATCH 27/37] Add coverage pragmas.

---
 drexml/cli/cli.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/drexml/cli/cli.py b/drexml/cli/cli.py
index d26d767..9d7db37 100644
--- a/drexml/cli/cli.py
+++ b/drexml/cli/cli.py
@@ -332,7 +332,8 @@ def plot(ctx, sel_path, score_path, stability_path, output_folder, gene):
         sel_mat=sel_path, score_mat=score_path, stab_mat=stability_path
     )
 
-    if gene:
+    # Tests already covered in plotting.
+    if gene: # pragma: no cover
         try:
             results.plot_gene_profile(gene=gene, output_folder=output_folder)
         except KeyError as kerr:

From 21c874f26002156f7468bdc0c57734f53b5ea471 Mon Sep 17 00:00:00 2001
From: Carlos Loucera <carlos.loucera@juntadeandalucia.es>
Date: Tue, 15 Aug 2023 11:04:39 +0200
Subject: [PATCH 28/37] Fix style.

---
 drexml/cli/cli.py      | 2 +-
 drexml/plotting.py     | 8 +++++---
 tests/test_plotting.py | 6 ++++--
 3 files changed, 10 insertions(+), 6 deletions(-)

diff --git a/drexml/cli/cli.py b/drexml/cli/cli.py
index 9d7db37..dc18c87 100644
--- a/drexml/cli/cli.py
+++ b/drexml/cli/cli.py
@@ -333,7 +333,7 @@ def plot(ctx, sel_path, score_path, stability_path, output_folder, gene):
     )
 
     # Tests already covered in plotting.
-    if gene: # pragma: no cover
+    if gene:  # pragma: no cover
         try:
             results.plot_gene_profile(gene=gene, output_folder=output_folder)
         except KeyError as kerr:
diff --git a/drexml/plotting.py b/drexml/plotting.py
index deb8239..51d5861 100644
--- a/drexml/plotting.py
+++ b/drexml/plotting.py
@@ -78,7 +78,9 @@ def filter_scores(self, remove_unstable=True):
 
         return scores_filt
 
-    def plot_relevance_heatmap(self, remove_unstable=True, output_folder=None): # pragma: no cover
+    def plot_relevance_heatmap(
+        self, remove_unstable=True, output_folder=None
+    ):  # pragma: no cover
         """
         Plot the relevance heatmap of the scores.
 
@@ -173,7 +175,7 @@ def plot_gene_profile(self, gene: str, output_folder=None):
                 output_folder.joinpath(f"{fname}.png"), dpi=300, bbox_inches="tight"
             )
             plt.savefig(output_folder.joinpath(f"{fname}.pdf"), bbox_inches="tight")
-        else: # pragma: no cover
+        else:  # pragma: no cover
             plt.show()
 
     def plot_metrics(self, width=2.735, output_folder=None):
@@ -252,5 +254,5 @@ def plot_metrics(self, width=2.735, output_folder=None):
             )
             plt.savefig(output_folder.joinpath(f"{fname}.pdf"), bbox_inches="tight")
 
-        else: # pragma: no cover
+        else:  # pragma: no cover
             plt.show()
diff --git a/tests/test_plotting.py b/tests/test_plotting.py
index efc473e..5b7b727 100644
--- a/tests/test_plotting.py
+++ b/tests/test_plotting.py
@@ -49,7 +49,9 @@ def test_plot_gene():
     this_gene = "3066"
     results.plot_gene_profile(output_folder=tmp_folder, gene=this_gene)
 
-    plot_files = [tmp_folder.joinpath(f"profile_{this_gene}.{ext}") for ext in PLOTTING_EXTENSIONS]
+    plot_files = [
+        tmp_folder.joinpath(f"profile_{this_gene}.{ext}") for ext in PLOTTING_EXTENSIONS
+    ]
 
     assert all([x.exists() for x in plot_files])
 
@@ -59,7 +61,7 @@ def test_plot_gene_fails():
     """
     Test that plot raises an error when a gene is not part of the relevance matrix.
     """
-    
+
     tmp_folder = pathlib.Path(tempfile.mkdtemp())
     results = setup_results()
 

From 2916ac7c11332ead0ec185d604e9794eba221900 Mon Sep 17 00:00:00 2001
From: Carlos Loucera <carlos.loucera@juntadeandalucia.es>
Date: Tue, 15 Aug 2023 11:06:20 +0200
Subject: [PATCH 29/37] Add option to retrieve the top list of genes according
 to the gda score.

---
 drexml/datasets.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/drexml/datasets.py b/drexml/datasets.py
index 33483a8..74cdaec 100644
--- a/drexml/datasets.py
+++ b/drexml/datasets.py
@@ -53,7 +53,7 @@ def load_disgenet():
     return disgenet
 
 
-def get_gene_disease_associations(disease_id):
+def get_gene_disease_associations(disease_id, k_top=40):
     """Retrieve the list of genes associated to a disese according to the Disgenet
     curated list of gene-disease associations.
 
@@ -62,13 +62,19 @@ def get_gene_disease_associations(disease_id):
     disease_id : str
         Disease ID.
 
+    k_top: int
+        Retrieve at most k_top genes based on the GDA score.
+
     Returns
     -------
     list
         List of gene IDs.
     """
     disgenet = load_disgenet()
-    return disgenet.loc[disgenet == disease_id].entrez_id.astype(str).unique().tolist()
+    disgenet = disgenet.loc[disgenet == disease_id]
+    disgenet = disgenet.nlargest(k_top, "dga_score")
+
+    return disgenet.entrez_id.astype(str).unique().tolist()
 
 
 def load_physiological_circuits():

From 4dc448dafe47a902ef6095394e96571d332e262f Mon Sep 17 00:00:00 2001
From: Carlos Loucera <carlos.loucera@juntadeandalucia.es>
Date: Tue, 15 Aug 2023 12:22:37 +0200
Subject: [PATCH 30/37] Add option to load gene seeds based on GDA scores.

---
 drexml/config.py    |  1 +
 drexml/datasets.py  | 14 +++++++++-----
 drexml/utils.py     | 34 ++++++++++++++++++++++++++++++++--
 tests/test_utils.py |  2 +-
 4 files changed, 43 insertions(+), 8 deletions(-)

diff --git a/drexml/config.py b/drexml/config.py
index 23cc170..90f2de7 100644
--- a/drexml/config.py
+++ b/drexml/config.py
@@ -6,6 +6,7 @@
 
 DEFAULT_DICT = {
     "seed_genes": None,
+    "disease_id": None,
     "use_physio": "true",
     "gene_exp": None,
     "gene_exp_zenodo": False,
diff --git a/drexml/datasets.py b/drexml/datasets.py
index 74cdaec..e29cccf 100644
--- a/drexml/datasets.py
+++ b/drexml/datasets.py
@@ -53,7 +53,7 @@ def load_disgenet():
     return disgenet
 
 
-def get_gene_disease_associations(disease_id, k_top=40):
+def get_gda(disease_id, k_top=40):
     """Retrieve the list of genes associated to a disese according to the Disgenet
     curated list of gene-disease associations.
 
@@ -71,7 +71,7 @@ def get_gene_disease_associations(disease_id, k_top=40):
         List of gene IDs.
     """
     disgenet = load_disgenet()
-    disgenet = disgenet.loc[disgenet == disease_id]
+    disgenet = disgenet.loc[disgenet["disease_id"] == disease_id]
     disgenet = disgenet.nlargest(k_top, "dga_score")
 
     return disgenet.entrez_id.astype(str).unique().tolist()
@@ -245,9 +245,12 @@ def preprocess_frame(res, env, key):
     elif key == "pathvals":
         return preprocess_activities(res)
     elif key == "circuits":
-        return preprocess_map(
-            res, env["seed_genes"], env["circuits_column"], env["use_physio"]
-        )
+        gene_list = []
+        if env["seed_genes"]:
+            gene_list += env["seed_genes"]
+        if env["disease_id"]:
+            gene_list += [str(gene) for gene in get_gda(env["disease_id"])]
+        return preprocess_map(res, gene_list, env["circuits_column"], env["use_physio"])
     elif key == "genes":
         return preprocess_genes(res, env["genes_column"])
 
@@ -350,6 +353,7 @@ def preprocess_map(frame, disease_seed_genes, circuits_column, use_physio):
     """
     frame.index = frame.index.str.replace("-", ".").str.replace(" ", ".")
     if disease_seed_genes:
+        print(disease_seed_genes)
         disease_seed_genes = frame.columns.intersection(disease_seed_genes)
         circuits = frame.index[frame[disease_seed_genes].any(axis=1)].tolist()
     else:
diff --git a/drexml/utils.py b/drexml/utils.py
index 4baf741..f1fa36f 100644
--- a/drexml/utils.py
+++ b/drexml/utils.py
@@ -314,6 +314,35 @@ def convert_names(dataset, keys, axis):
     return dataset
 
 
+def read_disease_id(config):
+    """Read disease id from config file. It expects a disease id using the UMLS.
+
+    Parameters
+    ----------
+    config : dict
+        Parsed config dict.
+
+    Returns
+    -------
+    dict
+        Updated config dict.
+
+    Raises
+    ------
+    ValueError
+        Raise error if format is unsupported.
+    """
+    try:
+        if config["disease_id"] is not None:
+            config["disease_id"] = str(config["disease_id"])
+    except ValueError as err:
+        print(err)
+        print("seed_genes should be a comma-separated list of genes.")
+        raise
+
+    return config
+
+
 def read_seed_genes(config):
     """Read seed genes from config file. It expect a comma-separated list of entrez ids.
 
@@ -537,6 +566,7 @@ def read_disease_config(disease):
             config[key] = DEFAULT_DICT[key]
 
     config = read_seed_genes(config)
+    config = read_disease_id(config)
     config = read_use_physio(config)
     config = read_activity_normalizer(config)
     config = read_path_based(config, key="gene_exp", data_path=env_parent_path)
@@ -817,8 +847,8 @@ def update_circuits(config):
     """
 
     if config["circuits"] is None:
-        if config["seed_genes"] is None:
-            raise ValueError("Either circuits or seed_genes list is required.")
+        if config["seed_genes"] is None and config["disease_id"] is None:
+            raise ValueError("Provide on of circuits, disease_id or gene_seeds.")
         if (config["GTEX_VERSION"] != DEFAULT_DICT["GTEX_VERSION"]) or (
             config["HIPATHIA_VERSION"] != DEFAULT_DICT["HIPATHIA_VERSION"]
         ):
diff --git a/tests/test_utils.py b/tests/test_utils.py
index 2bea47c..ea77afd 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -341,7 +341,7 @@ def test_read_circuits_column_fail():
 @pytest.mark.xfail(raises=(ValueError,))
 def test_update_circuits_fail():
     """Unit test that read_circuits_column raises an error."""
-    config = {"circuits": None, "seed_genes": None}
+    config = {"circuits": None, "seed_genes": None, "disease_id": None}
     utils.update_circuits(config)
 
 

From 8878eacf10ff8d2d73cfbbc6ff1661175ade834e Mon Sep 17 00:00:00 2001
From: Carlos Loucera <carlos.loucera@juntadeandalucia.es>
Date: Tue, 15 Aug 2023 12:26:39 +0200
Subject: [PATCH 31/37] Add FA example.

---
 examples/fanconi_anemia/experiment.env | 3 +++
 1 file changed, 3 insertions(+)
 create mode 100644 examples/fanconi_anemia/experiment.env

diff --git a/examples/fanconi_anemia/experiment.env b/examples/fanconi_anemia/experiment.env
new file mode 100644
index 0000000..a97ef79
--- /dev/null
+++ b/examples/fanconi_anemia/experiment.env
@@ -0,0 +1,3 @@
+disease_id="C0015625"
+use_physio=false
+activity_normalizer=true

From 93afd2389bdd19b538a70d58fe1bdd814452b49c Mon Sep 17 00:00:00 2001
From: Carlos Loucera <carlos.loucera@juntadeandalucia.es>
Date: Tue, 15 Aug 2023 12:34:31 +0200
Subject: [PATCH 32/37] Move specific plots to examples.

---
 {drexml => examples/fanconi_anemia}/boxplots_shap_scores.py       | 0
 .../fanconi_anemia}/drugbank-v050110_alldrugbyaction.tsv          | 0
 .../fanconi_anemia}/drugbank-v050110_mostdrugeffects_KDTs.tsv     | 0
 {drexml => examples/fanconi_anemia}/heatmap_shap_scores.py        | 0
 {drexml => examples/fanconi_anemia}/heatmap_top30KDT_drugs.py     | 0
 {drexml => examples/fanconi_anemia}/histograms_shap_scores.py     | 0
 {drexml => examples/fanconi_anemia}/shap_scores_filtering.py      | 0
 7 files changed, 0 insertions(+), 0 deletions(-)
 rename {drexml => examples/fanconi_anemia}/boxplots_shap_scores.py (100%)
 rename {drexml/resources => examples/fanconi_anemia}/drugbank-v050110_alldrugbyaction.tsv (100%)
 rename {drexml/resources => examples/fanconi_anemia}/drugbank-v050110_mostdrugeffects_KDTs.tsv (100%)
 rename {drexml => examples/fanconi_anemia}/heatmap_shap_scores.py (100%)
 rename {drexml => examples/fanconi_anemia}/heatmap_top30KDT_drugs.py (100%)
 rename {drexml => examples/fanconi_anemia}/histograms_shap_scores.py (100%)
 rename {drexml => examples/fanconi_anemia}/shap_scores_filtering.py (100%)

diff --git a/drexml/boxplots_shap_scores.py b/examples/fanconi_anemia/boxplots_shap_scores.py
similarity index 100%
rename from drexml/boxplots_shap_scores.py
rename to examples/fanconi_anemia/boxplots_shap_scores.py
diff --git a/drexml/resources/drugbank-v050110_alldrugbyaction.tsv b/examples/fanconi_anemia/drugbank-v050110_alldrugbyaction.tsv
similarity index 100%
rename from drexml/resources/drugbank-v050110_alldrugbyaction.tsv
rename to examples/fanconi_anemia/drugbank-v050110_alldrugbyaction.tsv
diff --git a/drexml/resources/drugbank-v050110_mostdrugeffects_KDTs.tsv b/examples/fanconi_anemia/drugbank-v050110_mostdrugeffects_KDTs.tsv
similarity index 100%
rename from drexml/resources/drugbank-v050110_mostdrugeffects_KDTs.tsv
rename to examples/fanconi_anemia/drugbank-v050110_mostdrugeffects_KDTs.tsv
diff --git a/drexml/heatmap_shap_scores.py b/examples/fanconi_anemia/heatmap_shap_scores.py
similarity index 100%
rename from drexml/heatmap_shap_scores.py
rename to examples/fanconi_anemia/heatmap_shap_scores.py
diff --git a/drexml/heatmap_top30KDT_drugs.py b/examples/fanconi_anemia/heatmap_top30KDT_drugs.py
similarity index 100%
rename from drexml/heatmap_top30KDT_drugs.py
rename to examples/fanconi_anemia/heatmap_top30KDT_drugs.py
diff --git a/drexml/histograms_shap_scores.py b/examples/fanconi_anemia/histograms_shap_scores.py
similarity index 100%
rename from drexml/histograms_shap_scores.py
rename to examples/fanconi_anemia/histograms_shap_scores.py
diff --git a/drexml/shap_scores_filtering.py b/examples/fanconi_anemia/shap_scores_filtering.py
similarity index 100%
rename from drexml/shap_scores_filtering.py
rename to examples/fanconi_anemia/shap_scores_filtering.py

From 4357ae242c4d6865482221dd5615280feb779f31 Mon Sep 17 00:00:00 2001
From: Carlos Loucera <carlos.loucera@juntadeandalucia.es>
Date: Tue, 15 Aug 2023 12:45:54 +0200
Subject: [PATCH 33/37] Move databases to resources.

---
 .../{ => resources}/drugbank-v050110_alldrugbyaction.tsv          | 0
 .../{ => resources}/drugbank-v050110_mostdrugeffects_KDTs.tsv     | 0
 2 files changed, 0 insertions(+), 0 deletions(-)
 rename examples/fanconi_anemia/{ => resources}/drugbank-v050110_alldrugbyaction.tsv (100%)
 rename examples/fanconi_anemia/{ => resources}/drugbank-v050110_mostdrugeffects_KDTs.tsv (100%)

diff --git a/examples/fanconi_anemia/drugbank-v050110_alldrugbyaction.tsv b/examples/fanconi_anemia/resources/drugbank-v050110_alldrugbyaction.tsv
similarity index 100%
rename from examples/fanconi_anemia/drugbank-v050110_alldrugbyaction.tsv
rename to examples/fanconi_anemia/resources/drugbank-v050110_alldrugbyaction.tsv
diff --git a/examples/fanconi_anemia/drugbank-v050110_mostdrugeffects_KDTs.tsv b/examples/fanconi_anemia/resources/drugbank-v050110_mostdrugeffects_KDTs.tsv
similarity index 100%
rename from examples/fanconi_anemia/drugbank-v050110_mostdrugeffects_KDTs.tsv
rename to examples/fanconi_anemia/resources/drugbank-v050110_mostdrugeffects_KDTs.tsv

From 86ad4fb47815898d881230bbe78389891e3d92c2 Mon Sep 17 00:00:00 2001
From: Carlos Loucera <carlos.loucera@juntadeandalucia.es>
Date: Tue, 15 Aug 2023 12:46:09 +0200
Subject: [PATCH 34/37] Keep rsults folder.

---
 examples/fanconi_anemia/results/.gitkeep | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 examples/fanconi_anemia/results/.gitkeep

diff --git a/examples/fanconi_anemia/results/.gitkeep b/examples/fanconi_anemia/results/.gitkeep
new file mode 100644
index 0000000..e69de29

From 94188e16ec70c37c2328c7941b6b311aa0634314 Mon Sep 17 00:00:00 2001
From: Carlos Loucera <carlos.loucera@juntadeandalucia.es>
Date: Tue, 15 Aug 2023 12:56:46 +0200
Subject: [PATCH 35/37] Move drug ora methods to examples.

---
 {drexml => examples/fanconi_anemia}/ORA_drugs.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename {drexml => examples/fanconi_anemia}/ORA_drugs.py (100%)

diff --git a/drexml/ORA_drugs.py b/examples/fanconi_anemia/ORA_drugs.py
similarity index 100%
rename from drexml/ORA_drugs.py
rename to examples/fanconi_anemia/ORA_drugs.py

From ddb0ceb730677e2efeb3dabddfa3c08e04af21c2 Mon Sep 17 00:00:00 2001
From: Carlos Loucera <carlos.loucera@juntadeandalucia.es>
Date: Tue, 15 Aug 2023 12:57:03 +0200
Subject: [PATCH 36/37] Add mwe for fanconi anemia.

---
 examples/fanconi_anemia/run.sh | 15 +++++++++++++++
 1 file changed, 15 insertions(+)
 create mode 100644 examples/fanconi_anemia/run.sh

diff --git a/examples/fanconi_anemia/run.sh b/examples/fanconi_anemia/run.sh
new file mode 100644
index 0000000..c37a9ff
--- /dev/null
+++ b/examples/fanconi_anemia/run.sh
@@ -0,0 +1,15 @@
+#!/usr/bin/env bash
+
+mkdir -p results
+
+conda create -y -p ./.venv python=3.10 -c conda-forge
+
+# install latest stable version fo drexml
+CONDA_RUN="conda run --live-stream --no-capture-output -p ./.venv"
+${CONDA_RUN} pip install drexml
+
+# run drexml using all CPUs and no GPUs
+${CONDA_RUN} drexml run --n-gpus 0 experiment.env > results/drexml.out 2> results/drexml.err
+rm -rf results/tmp
+
+# plot

From cd11a6da74cafd219640dcc070b19bb30411a41c Mon Sep 17 00:00:00 2001
From: Carlos Loucera <carlos.loucera@juntadeandalucia.es>
Date: Tue, 15 Aug 2023 17:26:46 +0200
Subject: [PATCH 37/37] Bump micro.

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index f4959fb..51674bc 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -18,7 +18,7 @@ dependencies = [
     "zenodo-client<0.3",
 ]
 name = "drexml"
-version = "0.11.7"
+version = "0.11.8"
 description = "(DRExM³L) Drug REpurposing using and eXplainable Machine Learning and Mechanistic Models of signal transduction\""
 readme = "README.md"