preprocessing scripts/REDOX Interference_ preprocessing script.py

#1. Import modules

pip install rdkit
pip install molvs
import pandas as pd
import numpy as np
import rdkit
import molvs
from rdkit import Chem

standardizer = molvs.Standardizer()
fragment_remover = molvs.fragment.FragmentRemover()

# 2. Convert the SDF file from the original paper into data frame
# Before running the code, please download SDF files from the original paper
# https://pubs.acs.org/doi/10.1021/acs.jmedchem.3c00482

from rdkit.Chem import PandasTools
sdfFile = 'Redox_training_set_curated.sdf'
dataframe = PandasTools.LoadSDF(sdfFile)
dataframe.to_csv('redox.csv', index=False)
df = pd.read_csv('redox.csv')

# 3. Resolve SMILES parse error
# Some of the 'Raw_SMILES' rows contain TWO SMILES separated by ';'' and, they cause SMILES parse error (which means they cannot be read)
# So we separated the SMILES and renamed the columns

df.rename(columns = {'PUBCHEM_EXT_DATASOURCE_REGID': 'REGID_1'}, inplace = True)
df.rename(columns = {'Other REGIDs': 'REGID_2'}, inplace = True)

df.insert(3, 'SMILES_2', np.NaN)
df['SMILES_2'] = df['Raw_SMILES'].str.split(';').str[1]
df['Raw_SMILES'] = df['Raw_SMILES'].str.split(';').str[0]
df.rename(columns= {'Raw_SMILES' : 'SMILES_1'}, inplace = True)

df.insert(10, 'AC50_uM_2', np.NaN)
df['AC50_uM_2'] = df['AC50_uM'].str.split(';').str[1]
df['AC50_uM'] = df['AC50_uM'].str.split(';').str[0]
df.rename(columns = {'AC50_uM': 'AC50_uM_1'}, inplace = True)

# 4. Sanitize with MolVS and print problems

df['X_1'] = [ \
    rdkit.Chem.MolToSmiles(
        fragment_remover.remove(
        standardizer.standardize(
        rdkit.Chem.MolFromSmiles(
        smiles))))
    for smiles in df['SMILES_1']]

def process_smiles(smiles):
    if pd.isna(smiles):
        return None  
    try:
        return rdkit.Chem.MolToSmiles(
            fragment_remover.remove(
                standardizer.standardize(
                    rdkit.Chem.MolFromSmiles(smiles))))
    except Exception as e:
        print(f"Error processing SMILES {smiles}: {e}")
        return None  

df['X_2'] = df['SMILES_2'].apply(process_smiles)

# 5. Rename the columns

df.rename(columns={'X_1' : 'newSMILES_1', 'X_2' : 'newSMILES_2'}, inplace = True)

# 6. Create a file with sanitized SMILES

df[['REGID_1',
 'REGID_2',
 'newSMILES_1',
 'newSMILES_2',      
 'log_AC50_M',
 'Efficacy',
 'CC-v2',
 'Outcome',
 'InChIKey',
 'AC50_uM_1',
 'AC50_uM_2',
 'ID',
 'ROMol']].to_csv('redox_sanitized.csv', index = False)