1_query_chembl.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyPzncOV+4nE5l0Czci3SadB",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/shumshersubashgautam/Computer-Aided-Drug-Discovery-Learning/blob/main/1_query_chembl.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1WxWRzNdL8Fl",
        "outputId": "de3157fa-9340-4a02-d92b-e28b98e8035f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'teachopencadd'...\n",
            "remote: Enumerating objects: 17115, done.\u001b[K\n",
            "remote: Counting objects: 100% (2093/2093), done.\u001b[K\n",
            "remote: Compressing objects: 100% (969/969), done.\u001b[K\n",
            "remote: Total 17115 (delta 1309), reused 1786 (delta 1122), pack-reused 15022\u001b[K\n",
            "Receiving objects: 100% (17115/17115), 849.77 MiB | 17.17 MiB/s, done.\n",
            "Resolving deltas: 100% (11506/11506), done.\n",
            "Updating files: 100% (468/468), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/volkamerlab/teachopencadd.git"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this notebook, we will learn more about the ChEMBL database and how to extract data from ChEMBL, i.e. (compound, activity data) pairs for a target of interest. These data sets can be used for many cheminformatics tasks, such as similarity search, clustering or machine learning.\n",
        "\n",
        "Our work here will include finding compounds that were tested against a certain target and filtering available bioactivity data.\n",
        "\n",
        "# **Contents in Theory**\n",
        "ChEMBL database\n",
        "\n",
        "ChEMBL web services\n",
        "\n",
        "ChEMBL web resource client\n",
        "\n",
        "Compound activity measures\n",
        "\n",
        "IC50 measure\n",
        "\n",
        "pIC50 value\n",
        "\n",
        "# **Contents in Practical**\n",
        "Goal: Get a list of compounds with bioactivity data for a given target\n",
        "\n",
        "Connect to ChEMBL database\n",
        "\n",
        "Get target data (example: EGFR kinase)\n",
        "\n",
        "Fetch and download target data\n",
        "\n",
        "Select target ChEMBL ID\n",
        "\n",
        "Get bioactivity data\n",
        "\n",
        "Fetch and download bioactivity data for targets\n",
        "\n",
        "Preprocess and filter bioactivity data\n",
        "\n",
        "Get compound data\n",
        "\n",
        "Fetch and download compound data\n",
        "\n",
        "Preprocess and filter compound data\n",
        "\n",
        "Output bioactivity-compound data\n",
        "\n",
        "Merge bioactivity and compound data, and add pIC50 values\n",
        "\n",
        "Draw molecules with highest pIC50\n",
        "\n",
        "Freeze bioactivity data to ChEMBL 27\n",
        "\n",
        "Write output file\n",
        "\n",
        "# **References**\n",
        "ChEMBL bioactivity database: Gaulton et al., Nucleic Acids Res. (2017), 45(Database issue), D945–D954\n",
        "ChEMBL web services: Davies et al., Nucleic Acids Res. (2015), 43, 612-620\n",
        "ChEMBL web-interface\n",
        "GitHub ChEMBL web rescource client\n",
        "The EBI RDF platform: Jupp et al., Bioinformatics (2014), 30(9), 1338-9\n",
        "Info on half maximal inhibitory concentration: (p)IC50\n",
        "UniProt website"
      ],
      "metadata": {
        "id": "b1wn_vpHMkOU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **ChEMBL database**\n",
        "\"ChEMBL is a manually curated database of bioactive molecules with drug-like properties. It brings together chemical, bioactivity and genomic data to aid the translation of genomic information into effective new drugs.\" (ChEMBL website)\n",
        "\n",
        "Open large-scale bioactivity database\n",
        "Current data content (as of 09.2020, ChEMBL 27):\n",
        ">1.9 million distinct compounds\n",
        ">16 million activity values\n",
        "Assays are mapped to ~13,000 targets\n",
        "Data sources include scientific literature, PubChem bioassays, Drugs for Neglected Diseases Initiative (DNDi), BindingDB database, ...\n",
        "ChEMBL data can be accessed via a web-interface, the EBI-RDF platform and the ChEMBL web rescource client"
      ],
      "metadata": {
        "id": "jYYjsn-WNJSN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Compound activity measures**\n",
        "IC50 measure\n",
        "\n",
        "Half maximal inhibitory concentration\n",
        "\n",
        "Indicates how much of a particular drug or other substance is needed to inhibit a given biological process by half\n"
      ],
      "metadata": {
        "id": "qzncNoa2NRX9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![Wiki_Example_IC50_curve_demonstrating_visually_how_IC50_is_derived.png](data:image/png;base64,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)\n",
        "Figure 2: Visual demonstration of how to derive an IC50 value: (i) Arrange inhibition data on y-axis and log(concentration) on x-axis. (ii) Identify maximum and minimum inhibition. (iii) The IC50 is the concentration at which the curve passes through the 50% inhibition level. Figure \"Example IC50 curve demonstrating visually how IC50 is derived\" by JesseAlanGordon is licensed under CC BY-SA 3.0.\n",
        "\n",
        "pIC50 value\n",
        "To facilitate the comparison of IC50 values, which have a large value range and are given in different units (M, nM, ...), often pIC50 values are used\n",
        "The pIC50 is the negative log of the IC50 value when converted to molar units:\n",
        ", where\n",
        " is specified in units of M\n",
        "Higher pIC50 values indicate exponentially greater potency of the drug\n",
        "Note that the conversion can be adapted to the respective IC50 unit, e.g. for nM:\n",
        "Other activity measures:\n",
        "\n",
        "Besides, IC50 and pIC50, other bioactivity measures are used, such as the equilibrium constant KI and the half-maximal effective concentration EC50.\n",
        "\n",
        "Practical\n",
        "In the following, we want to download all molecules that have been tested against our target of interest, the epidermal growth factor receptor (EGFR) kinase.\n",
        "\n",
        "Connect to ChEMBL database\n",
        "First, the ChEMBL web resource client as well as other Python libraries are imported."
      ],
      "metadata": {
        "id": "u8TVLtbKNiWV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install rdkit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4BYPAfF1N3Al",
        "outputId": "e79d7a16-d849-4a73-91cd-9441e66f61d3"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting rdkit\n",
            "  Downloading rdkit-2023.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (29.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m29.7/29.7 MB\u001b[0m \u001b[31m45.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from rdkit) (1.22.4)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from rdkit) (8.4.0)\n",
            "Installing collected packages: rdkit\n",
            "Successfully installed rdkit-2023.3.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install chembl-webresource-client"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ljMoqSYtOEtt",
        "outputId": "1c4ff35d-28bd-4b68-ca26-bb64ecc9c299"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting chembl-webresource-client\n",
            "  Downloading chembl_webresource_client-0.10.8-py3-none-any.whl (55 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.2/55.2 kB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from chembl-webresource-client) (1.26.15)\n",
            "Requirement already satisfied: requests>=2.18.4 in /usr/local/lib/python3.10/dist-packages (from chembl-webresource-client) (2.27.1)\n",
            "Collecting requests-cache~=0.7.0 (from chembl-webresource-client)\n",
            "  Downloading requests_cache-0.7.5-py3-none-any.whl (39 kB)\n",
            "Requirement already satisfied: easydict in /usr/local/lib/python3.10/dist-packages (from chembl-webresource-client) (1.10)\n",
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            "Collecting url-normalize<2.0,>=1.4 (from requests-cache~=0.7.0->chembl-webresource-client)\n",
            "  Downloading url_normalize-1.4.3-py2.py3-none-any.whl (6.8 kB)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from url-normalize<2.0,>=1.4->requests-cache~=0.7.0->chembl-webresource-client) (1.16.0)\n",
            "Installing collected packages: url-normalize, attrs, requests-cache, chembl-webresource-client\n",
            "  Attempting uninstall: attrs\n",
            "    Found existing installation: attrs 23.1.0\n",
            "    Uninstalling attrs-23.1.0:\n",
            "      Successfully uninstalled attrs-23.1.0\n",
            "Successfully installed attrs-21.4.0 chembl-webresource-client-0.10.8 requests-cache-0.7.5 url-normalize-1.4.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "from pathlib import Path\n",
        "from zipfile import ZipFile\n",
        "from tempfile import TemporaryDirectory\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from rdkit.Chem import PandasTools\n",
        "from chembl_webresource_client.new_client import new_client\n",
        "from tqdm.auto import tqdm"
      ],
      "metadata": {
        "id": "ozSBXI6OMOLw"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "HERE = Path(_dh[-1])\n",
        "DATA = HERE / \"data\""
      ],
      "metadata": {
        "id": "90jtsVXMN-3J"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "targets_api = new_client.target\n",
        "compounds_api = new_client.molecule\n",
        "bioactivities_api = new_client.activity"
      ],
      "metadata": {
        "id": "VSerZQDUOSdv"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(targets_api)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mg0m17cgOVkz",
        "outputId": "216c916d-aeb3-4846-ecf6-7b0eb0be829b"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "chembl_webresource_client.query_set.QuerySet"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Get target data (EGFR kinase)**\n",
        "Get UniProt ID of the target of interest (EGFR kinase: P00533) from UniProt website\n",
        "Use UniProt ID to get target information\n",
        "Select a different UniProt ID, if you are interested in another target."
      ],
      "metadata": {
        "id": "1mLvfTWMObcd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "uniprot_id = \"P00533\""
      ],
      "metadata": {
        "id": "R8mueQlyOYS6"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fetch target data from ChEMBL"
      ],
      "metadata": {
        "id": "HInQ9964OhWt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get target information from ChEMBL but restrict it to specified values only\n",
        "targets = targets_api.get(target_components__accession=uniprot_id).only(\n",
        "    \"target_chembl_id\", \"organism\", \"pref_name\", \"target_type\"\n",
        ")\n",
        "print(f'The type of the targets is \"{type(targets)}\"')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sfs_K8ypOfWT",
        "outputId": "f1cd2d85-9831-4b36-93ba-15bb12d19d86"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The type of the targets is \"<class 'chembl_webresource_client.query_set.QuerySet'>\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Download target data from ChEMBL**\n",
        "The results of the query are stored in targets, a QuerySet, i.e. the results are not fetched from ChEMBL until we ask for it (here using pandas.DataFrame.from_records).\n",
        "\n",
        "More information about the QuerySet datatype:\n",
        "\n",
        "QuerySets are lazy – the act of creating a QuerySet does not involve any database activity. You can stack filters together all day long, and Django will actually not run the query until the QuerySet is evaluated.\n",
        "\n",
        "(querysets-are-lazy)"
      ],
      "metadata": {
        "id": "B3SWSowVOm5N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "targets = pd.DataFrame.from_records(targets)\n",
        "targets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "oA3tEZtcOjlA",
        "outputId": "1f0d5a58-b296-4e85-fda9-be1409fc228d"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       organism                                          pref_name  \\\n",
              "0  Homo sapiens             Epidermal growth factor receptor erbB1   \n",
              "1  Homo sapiens             Epidermal growth factor receptor erbB1   \n",
              "2  Homo sapiens  Epidermal growth factor receptor and ErbB2 (HE...   \n",
              "3  Homo sapiens                   Epidermal growth factor receptor   \n",
              "4  Homo sapiens  MER intracellular domain/EGFR extracellular do...   \n",
              "5  Homo sapiens  Protein cereblon/Epidermal growth factor receptor   \n",
              "6  Homo sapiens                                        EGFR/PPP1CA   \n",
              "7  Homo sapiens                                           VHL/EGFR   \n",
              "8  Homo sapiens  Baculoviral IAP repeat-containing protein 2/Ep...   \n",
              "\n",
              "  target_chembl_id                  target_type  \n",
              "0        CHEMBL203               SINGLE PROTEIN  \n",
              "1        CHEMBL203               SINGLE PROTEIN  \n",
              "2    CHEMBL2111431               PROTEIN FAMILY  \n",
              "3    CHEMBL2363049               PROTEIN FAMILY  \n",
              "4    CHEMBL3137284             CHIMERIC PROTEIN  \n",
              "5    CHEMBL4523680  PROTEIN-PROTEIN INTERACTION  \n",
              "6    CHEMBL4523747  PROTEIN-PROTEIN INTERACTION  \n",
              "7    CHEMBL4523998  PROTEIN-PROTEIN INTERACTION  \n",
              "8    CHEMBL4802031  PROTEIN-PROTEIN INTERACTION  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6b5730d1-7713-4ffc-a11e-4581bc902b27\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
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              "\n",
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              "      <th></th>\n",
              "      <th>organism</th>\n",
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              "      <td>Homo sapiens</td>\n",
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              "      <th>1</th>\n",
              "      <td>Homo sapiens</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Homo sapiens</td>\n",
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              "      <td>CHEMBL2111431</td>\n",
              "      <td>PROTEIN FAMILY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Homo sapiens</td>\n",
              "      <td>Epidermal growth factor receptor</td>\n",
              "      <td>CHEMBL2363049</td>\n",
              "      <td>PROTEIN FAMILY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Homo sapiens</td>\n",
              "      <td>MER intracellular domain/EGFR extracellular do...</td>\n",
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              "      <th>5</th>\n",
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              "      <th>6</th>\n",
              "      <td>Homo sapiens</td>\n",
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              "      <td>PROTEIN-PROTEIN INTERACTION</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Homo sapiens</td>\n",
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              "      <td>PROTEIN-PROTEIN INTERACTION</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Homo sapiens</td>\n",
              "      <td>Baculoviral IAP repeat-containing protein 2/Ep...</td>\n",
              "      <td>CHEMBL4802031</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Select target (target ChEMBL ID)**\n",
        "After checking the entries, we select the first entry as our target of interest:\n",
        "\n",
        "CHEMBL203: It is a single protein and represents the human Epidermal growth factor receptor (EGFR, also named erbB1)"
      ],
      "metadata": {
        "id": "602lHkdQOx6f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "target = targets.iloc[0]\n",
        "target"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HdH9eL8JOqf8",
        "outputId": "20d6d382-2a1a-4933-9624-8f67d182b4e7"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "organism                                      Homo sapiens\n",
              "pref_name           Epidermal growth factor receptor erbB1\n",
              "target_chembl_id                                 CHEMBL203\n",
              "target_type                                 SINGLE PROTEIN\n",
              "Name: 0, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chembl_id = target.target_chembl_id\n",
        "print(f\"The target ChEMBL ID is {chembl_id}\")\n",
        "# NBVAL_CHECK_OUTPUT"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "olIXUO2pO26X",
        "outputId": "b15d316c-339f-453e-87d5-b47ef1ff9f93"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The target ChEMBL ID is CHEMBL203\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Get bioactivity data**\n",
        "Now, we want to query bioactivity data for the target of interest.\n",
        "\n",
        "Fetch bioactivity data for the target from ChEMBL\n",
        "In this step, we fetch the bioactivity data and filter it to only consider\n",
        "\n",
        "human proteins,\n",
        "bioactivity type IC50,\n",
        "exact measurements (relation '='), and\n",
        "binding data (assay type 'B')."
      ],
      "metadata": {
        "id": "LQpsDcrrO9BH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bioactivities = bioactivities_api.filter(\n",
        "    target_chembl_id=chembl_id, type=\"IC50\", relation=\"=\", assay_type=\"B\"\n",
        ").only(\n",
        "    \"activity_id\",\n",
        "    \"assay_chembl_id\",\n",
        "    \"assay_description\",\n",
        "    \"assay_type\",\n",
        "    \"molecule_chembl_id\",\n",
        "    \"type\",\n",
        "    \"standard_units\",\n",
        "    \"relation\",\n",
        "    \"standard_value\",\n",
        "    \"target_chembl_id\",\n",
        "    \"target_organism\",\n",
        ")\n",
        "\n",
        "print(f\"Length and type of bioactivities object: {len(bioactivities)}, {type(bioactivities)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8plHBxakO5nD",
        "outputId": "65bd0683-3995-48f9-b2b6-e76331470a16"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Length and type of bioactivities object: 10420, <class 'chembl_webresource_client.query_set.QuerySet'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Each entry in our bioactivity set holds the following information:"
      ],
      "metadata": {
        "id": "H7Se-6wcPERd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Length and type of first element: {len(bioactivities[0])}, {type(bioactivities[0])}\")\n",
        "bioactivities[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dw5vIX57PBNp",
        "outputId": "a260b5f5-9415-4ca4-beea-6ea7ba4c0fb0"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Length and type of first element: 13, <class 'dict'>\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'activity_id': 32260,\n",
              " 'assay_chembl_id': 'CHEMBL674637',\n",
              " 'assay_description': 'Inhibitory activity towards tyrosine phosphorylation for the epidermal growth factor-receptor kinase',\n",
              " 'assay_type': 'B',\n",
              " 'molecule_chembl_id': 'CHEMBL68920',\n",
              " 'relation': '=',\n",
              " 'standard_units': 'nM',\n",
              " 'standard_value': '41.0',\n",
              " 'target_chembl_id': 'CHEMBL203',\n",
              " 'target_organism': 'Homo sapiens',\n",
              " 'type': 'IC50',\n",
              " 'units': 'uM',\n",
              " 'value': '0.041'}"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Download bioactivity data from ChEMBL\n",
        "Finally, we download the QuerySet in the form of a pandas DataFrame.\n",
        "\n",
        "Note: This step should not take more than 2 minutes, if so try to rerun all cells starting from \"Fetch bioactivity data for the target from ChEMBL\" or read this message below:\n",
        "\n",
        "Load a local version of the data (in case you encounter any problems while fetching the data)\n",
        "If you experience difficulties to query the ChEMBL database, we also provide the resulting dataframe you will construct in the cell below. If you want to use the saved version, use the following code instead to obtain bioactivities_df"
      ],
      "metadata": {
        "id": "MWzYuyOTPMbF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bioactivities_df = pd.DataFrame.from_dict(bioactivities)\n",
        "print(f\"DataFrame shape: {bioactivities_df.shape}\")\n",
        "bioactivities_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 358
        },
        "id": "OoKyn49wPGr5",
        "outputId": "34b0e01b-77cc-4d63-b460-1cfdd3975566"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-19-6d3402cc7f6c>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbioactivities_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbioactivities\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"DataFrame shape: {bioactivities_df.shape}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mbioactivities_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mfrom_dict\u001b[0;34m(cls, data, orient, dtype, columns)\u001b[0m\n\u001b[1;32m   1762\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1763\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0morient\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"tight\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1764\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1765\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1766\u001b[0m             \u001b[0mrealdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"data\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m    736\u001b[0m                     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    737\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 738\u001b[0;31m                     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    739\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    740\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mis_dataclass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/chembl_webresource_client/query_set.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    125\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__next__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 127\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;31m#-----------------------------------------------------------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/chembl_webresource_client/query_set.py\u001b[0m in \u001b[0;36mnext\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    113\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchunk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_page\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchunk\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrent_index\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchunk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchunk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnext_page\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    116\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchunk\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/chembl_webresource_client/url_query.py\u001b[0m in \u001b[0;36mnext_page\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    432\u001b[0m         \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    433\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrent_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlimit\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrent_page\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 434\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_page\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    435\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    436\u001b[0m \u001b[0;31m# ----------------------------------------------------------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/chembl_webresource_client/url_query.py\u001b[0m in \u001b[0;36mget_page\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    387\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prepare_url_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    388\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 389\u001b[0;31m                 \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpost\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase_url\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'.'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrmt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    390\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    391\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/requests/sessions.py\u001b[0m in \u001b[0;36mpost\u001b[0;34m(self, url, data, json, **kwargs)\u001b[0m\n\u001b[1;32m    575\u001b[0m         \"\"\"\n\u001b[1;32m    576\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 577\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'POST'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    578\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/requests_cache/session.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, json, expire_after, **kwargs)\u001b[0m\n\u001b[1;32m     99\u001b[0m         \"\"\"\n\u001b[1;32m    100\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest_expire_after\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpire_after\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpatch_form_boundary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m             return super().request(\n\u001b[0m\u001b[1;32m    102\u001b[0m                 \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m                 \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/requests/sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    527\u001b[0m         }\n\u001b[1;32m    528\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 529\u001b[0;31m         \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    530\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    531\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/requests_cache/session.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;31m# If the cache is disabled, doesn't have the response, or it's expired, then fetch a new one\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mresponse\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m             \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_and_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    133\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mis_expired\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mold_data_on_error\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m             \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_resend_and_ignore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/requests_cache/session.py\u001b[0m in \u001b[0;36m_send_and_cache\u001b[0;34m(self, request, actions, **kwargs)\u001b[0m\n\u001b[1;32m    154\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_send_and_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mPreparedRequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactions\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mCacheActions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m         \u001b[0;34m\"\"\"Send the request and cache the response, unless disabled by settings or headers\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 156\u001b[0;31m         \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    157\u001b[0m         \u001b[0mactions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_from_response\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    643\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    644\u001b[0m         \u001b[0;31m# Send the request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 645\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    646\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    647\u001b[0m         \u001b[0;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    438\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mchunked\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 440\u001b[0;31m                 resp = conn.urlopen(\n\u001b[0m\u001b[1;32m    441\u001b[0m                     \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    442\u001b[0m                     \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m    701\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    702\u001b[0m             \u001b[0;31m# Make the request on the httplib connection object.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 703\u001b[0;31m             httplib_response = self._make_request(\n\u001b[0m\u001b[1;32m    704\u001b[0m                 \u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    705\u001b[0m                 \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    384\u001b[0m         \u001b[0;31m# Trigger any extra validation we need to do.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    385\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 386\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_conn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    387\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSocketTimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBaseSSLError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    388\u001b[0m             \u001b[0;31m# Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_validate_conn\u001b[0;34m(self, conn)\u001b[0m\n\u001b[1;32m   1040\u001b[0m         \u001b[0;31m# Force connect early to allow us to validate the connection.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"sock\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# AppEngine might not have  `.sock`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1042\u001b[0;31m             \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1043\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1044\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_verified\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    417\u001b[0m             \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_default_certs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    418\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 419\u001b[0;31m         self.sock = ssl_wrap_socket(\n\u001b[0m\u001b[1;32m    420\u001b[0m             \u001b[0msock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    421\u001b[0m             \u001b[0mkeyfile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkey_file\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/urllib3/util/ssl_.py\u001b[0m in \u001b[0;36mssl_wrap_socket\u001b[0;34m(sock, keyfile, certfile, cert_reqs, ca_certs, server_hostname, ssl_version, ciphers, ssl_context, ca_cert_dir, key_password, ca_cert_data, tls_in_tls)\u001b[0m\n\u001b[1;32m    447\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    448\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0msend_sni\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m         ssl_sock = _ssl_wrap_socket_impl(\n\u001b[0m\u001b[1;32m    450\u001b[0m             \u001b[0msock\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtls_in_tls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mserver_hostname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mserver_hostname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    451\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/urllib3/util/ssl_.py\u001b[0m in \u001b[0;36m_ssl_wrap_socket_impl\u001b[0;34m(sock, ssl_context, tls_in_tls, server_hostname)\u001b[0m\n\u001b[1;32m    491\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mserver_hostname\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 493\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mssl_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrap_socket\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msock\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mserver_hostname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mserver_hostname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    494\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mssl_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrap_socket\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msock\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/ssl.py\u001b[0m in \u001b[0;36mwrap_socket\u001b[0;34m(self, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, session)\u001b[0m\n\u001b[1;32m    511\u001b[0m         \u001b[0;31m# SSLSocket class handles server_hostname encoding before it calls\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    512\u001b[0m         \u001b[0;31m# ctx._wrap_socket()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 513\u001b[0;31m         return self.sslsocket_class._create(\n\u001b[0m\u001b[1;32m    514\u001b[0m             \u001b[0msock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msock\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    515\u001b[0m             \u001b[0mserver_side\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mserver_side\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/ssl.py\u001b[0m in \u001b[0;36m_create\u001b[0;34m(cls, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, context, session)\u001b[0m\n\u001b[1;32m   1069\u001b[0m                         \u001b[0;31m# non-blocking\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1070\u001b[0m                         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"do_handshake_on_connect should not be specified for non-blocking sockets\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1071\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_handshake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1072\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mOSError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1073\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/ssl.py\u001b[0m in \u001b[0;36mdo_handshake\u001b[0;34m(self, block)\u001b[0m\n\u001b[1;32m   1340\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0.0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mblock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1341\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msettimeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1342\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_handshake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1343\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1344\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msettimeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note that the first two rows describe the same bioactivity entry; we will remove such artifacts later during the deduplication step. Note also that we have columns for standard_units/units and standard_values/values; in the following, we will use the standardized columns (standardization by ChEMBL), and thus, we drop the other two columns.\n",
        "\n",
        "If we used the units and values columns, we would need to convert all values with many different units to nM:"
      ],
      "metadata": {
        "id": "g30XO2juPYn3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bioactivities_df[\"units\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 171
        },
        "id": "nKqQw1eLPQHD",
        "outputId": "6d41d81c-d5f2-4cdd-a553-8710ebe6797b"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-18-6aa6d80c3569>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbioactivities_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"units\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'bioactivities_df' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bioactivities_df.drop([\"units\", \"value\"], axis=1, inplace=True)\n",
        "bioactivities_df.head()"
      ],
      "metadata": {
        "id": "_CNPnV4CPdI2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Preprocess and filter bioactivity data\n",
        "Convert standard_value's datatype from object to float\n",
        "Delete entries with missing values\n",
        "Keep only entries with standard_unit == nM\n",
        "Delete duplicate molecules\n",
        "Reset DataFrame index\n",
        "Rename columns\n",
        "1. Convert datatype of \"standard_value\" from \"object\" to \"float\"\n",
        "\n",
        "The field standard_value holds standardized (here IC50) values. In order to make these values usable in calculations later on, convert values to floats."
      ],
      "metadata": {
        "id": "CZZf_CI8Pf5I"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iOyqe8u1Phvu"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}