microbe command line script initialized

microberx_cli.py

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+import os
+import multiprocessing
+import argparse
+import pandas as pd
+from microberx import MetabolitePredictor
+from rdkit import Chem
+from tqdm import tqdm
+import warnings
+
+warnings.filterwarnings("ignore")
+
+"""
+MicrobeRX Predictions Script
+=============================
+
+This script allows users to perform predictions using the MicrobeRX library either with a single smiles string or a file containing multiple smiles strings. The predictions can be customized with various parameters such as the biosystem, cutoff value, number of processing cores, and output directory.
+
+Usage
+-----
+To run the script, use the following command format:
+
+```bash
+python microberx_cli.py --smiles <smiles_string> --query_name <query_name> --biosystem <biosystem> --cutoff <cutoff_value> --out <output_directory>
+
+python microberx_cli.py --file <input_file> --biosystem <biosystem> --cutoff <cutoff_value> --num_cores <num_cores> --out <output_directory>
+
+    Arguments
+        --smiles : str, optional
+        Input smiles string for prediction. If provided, predictions will be made for this single molecule.
+
+        --query_name : str, optional
+        Query name for the provided smiles string. This name will be used in the output file name.
+
+        --file : str, optional
+        Input file containing multiple smiles strings and names. The file should be in tab-separated values (TSV) format with columns "name" and "smiles".
+
+        --biosystem : str, optional
+        Biosystem to use for predictions. Default is 'all'.
+
+        --cutoff : float, optional
+        Cutoff value for predictions. Default is 0.6.
+
+        --num_cores : int, optional
+        Number of cores to use for multiprocessing. Default is 4.
+
+        --out : str, optional
+        Output directory for predictions. Default is the current directory ('./').
+
+    Description
+        The script can be run in two modes:
+
+            - Single Prediction Mode: When the --smiles argument is provided, the script will perform predictions for the single molecule specified by the smiles string.
+            - Batch Prediction Mode: When the --file argument is provided, the script will read the input file and perform predictions for each molecule listed in the file using multiprocessing.
+    
+    Examples
+        Single Prediction Mode:
+            
+            python microberx_cli.py --SMILES "CCO" --query_name "ethanol" --biosystem "all" --cutoff 0.6 --out "./predictions/"
+        
+            This command will perform predictions for ethanol and save the results in the specified output directory.
+
+        Batch Prediction Mode:
+            
+            python microberx_cli.py --file "input_molecules.tsv" --biosystem "all" --cutoff 0.6 --num_cores 4 --out "./predictions/"
+            
+            This command will read the input_molecules.tsv file, perform predictions for each molecule using 4 cores, and save the results in the specified output directory.
+            
+    Output
+        The predictions are saved as TSV files in the specified output directory. Each file is named after the query name provided or the names listed in the input file.      
+"""
+
+def main():
+    parser = argparse.ArgumentParser(description='MicrobeRX Predictions')
+    parser.add_argument('--smiles', type=str, default="", help='Input smiles string for prediction')
+    parser.add_argument('--query_name', type=str, default="", help='Query name for the smiles string')
+    parser.add_argument('--file', type=str, default="", help='Input file for predictions')
+    parser.add_argument('--biosystem', type=str, default='all', help='Biosystem to use for predictions options: all, human, gutmicrobes')
+    parser.add_argument('--cutoff', type=float, default=0.6, help='Cutoff for predictions')
+    parser.add_argument('--num_cores', type=int, default=4, help='Number of cores to use for multiprocessing')
+    parser.add_argument('--out', type=str, default='./', help='Output directory for predictions')
+
+    args = parser.parse_args()
+
+    smiles = args.smiles
+    query_name = args.query_name
+    file = args.file
+    biosystem = args.biosystem
+    cutoff = args.cutoff
+    num_cores = args.num_cores
+    out = args.out
+
+    if smiles:
+        query = Chem.MolFromSmiles(smiles)
+
+        Predictor = MetabolitePredictor(query, query_name=query_name, biosystem=biosystem, cut_off=cutoff)
+        Predictor.run_prediction()
+
+        metabolites = Predictor.predicted_metabolites
+
+        output_file = os.path.join(out, f"{query_name}.tsv")
+        metabolites.to_csv(output_file, sep='\t', index=False)
+        print(f"Predictions saved to {output_file}")
+
+    elif file:
+        mols = pd.read_csv(file, sep="\t")
+
+        def _runPredictions(index):
+            try:
+                name = mols.name[index]
+                query = Chem.MolFromSmiles(mols.smiles[index])
+
+                Predictor = MetabolitePredictor(query, query_name=name, biosystem=biosystem, cut_off=cutoff)
+                Predictor.run_prediction()
+
+                output_file = os.path.join(out, f"{name}.tsv")
+                Predictor.predicted_metabolites.to_csv(output_file, sep='\t', index=False)
+
+            except Exception as e:
+                print(f"Error processing {mols.name[index]}: {e}")
+
+        with multiprocessing.Pool(num_cores) as pool:
+            list(tqdm(pool.imap(_runPredictions, mols.index), total=len(mols.index)))
+
+if __name__ == '__main__':
+    main()