Datasets:
Delete REDOX Interference_ preprocessing script.py
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REDOX Interference_ preprocessing script.py
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#1. Import modules
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pip install rdkit
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pip install molvs
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import pandas as pd
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import numpy as np
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import rdkit
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import molvs
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from rdkit import Chem
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standardizer = molvs.Standardizer()
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fragment_remover = molvs.fragment.FragmentRemover()
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# 2. Convert the SDF file from the original paper into data frame
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# Before running the code, please download SDF files from the original paper
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# https://pubs.acs.org/doi/10.1021/acs.jmedchem.3c00482
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from rdkit.Chem import PandasTools
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sdfFile = 'Redox_training_set_curated.sdf'
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dataframe = PandasTools.LoadSDF(sdfFile)
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dataframe.to_csv('redox.csv', index=False)
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df = pd.read_csv('redox.csv')
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# 3. Resolve SMILES parse error
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# Some of the 'Raw_SMILES' rows contain TWO SMILES separated by ';'' and, they cause SMILES parse error (which means they cannot be read)
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# So we separated the SMILES and renamed the columns
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df.rename(columns = {'PUBCHEM_EXT_DATASOURCE_REGID': 'REGID_1'}, inplace = True)
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df.rename(columns = {'Other REGIDs': 'REGID_2'}, inplace = True)
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df.insert(3, 'SMILES_2', np.NaN)
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df['SMILES_2'] = df['Raw_SMILES'].str.split(';').str[1]
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df['Raw_SMILES'] = df['Raw_SMILES'].str.split(';').str[0]
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df.rename(columns= {'Raw_SMILES' : 'SMILES_1'}, inplace = True)
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df.insert(10, 'AC50_uM_2', np.NaN)
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df['AC50_uM_2'] = df['AC50_uM'].str.split(';').str[1]
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df['AC50_uM'] = df['AC50_uM'].str.split(';').str[0]
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df.rename(columns = {'AC50_uM': 'AC50_uM_1'}, inplace = True)
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# 4. Sanitize with MolVS and print problems
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df['X_1'] = [ \
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rdkit.Chem.MolToSmiles(
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fragment_remover.remove(
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standardizer.standardize(
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rdkit.Chem.MolFromSmiles(
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smiles))))
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for smiles in df['SMILES_1']]
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def process_smiles(smiles):
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if pd.isna(smiles):
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return None
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try:
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return rdkit.Chem.MolToSmiles(
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fragment_remover.remove(
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standardizer.standardize(
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rdkit.Chem.MolFromSmiles(smiles))))
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except Exception as e:
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print(f"Error processing SMILES {smiles}: {e}")
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return None
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df['X_2'] = df['SMILES_2'].apply(process_smiles)
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# 5. Rename the columns
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df.rename(columns={'X_1' : 'newSMILES_1', 'X_2' : 'newSMILES_2'}, inplace = True)
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# 6. Create a file with sanitized SMILES
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df[['REGID_1',
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'REGID_2',
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'newSMILES_1',
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'newSMILES_2',
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'log_AC50_M',
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'Efficacy',
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'CC-v2',
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'Outcome',
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'InChIKey',
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'AC50_uM_1',
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'AC50_uM_2',
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'ID',
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'ROMol']].to_csv('redox_sanitized.csv', index = False)
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