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Upload MSTI Thiol Interference preprocessing script.py
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MSTI Thiol Interference preprocessing script.py
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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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from rdkit.Chem import PandasTools
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sdfFile = 'Thiol_training_set_curated.sdf'
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dataframe = PandasTools.LoadSDF(sdfFile)
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dataframe.to_csv('thiol.csv', index=False)
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df = pd.read_csv('thiol.csv')
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# Some of the 'Raw_SMILES' rows contain TWO smiles separated by ;
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# These cause smiles parse error (which means they cannot be read)
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# So I separated the smiles
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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(2, 'REGID_3', np.NaN)
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df['REGID_3'] = df['REGID_2'].str.split(',').str[1]
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df['REGID_2'] = df['REGID_2'].str.split(',').str[0]
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df.insert(4, 'SMILES_2', np.NaN)
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df.insert(5, 'SMILES_3', np.NaN)
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df[['Raw_SMILES', 'SMILES_2', 'SMILES_3']] = df['Raw_SMILES'].str.split(';', expand=True)
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df.rename(columns= {'Raw_SMILES' : 'SMILES_1'}, inplace = True)
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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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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_3'] = df['SMILES_3'].apply(process_smiles)
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df.rename(columns={'X_1' : 'newSMILES_1', 'X_2' : 'newSMILES_2', 'X_3' : 'newSMILES_3'}, inplace = True)
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df[['REGID_1',
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'REGID_2',
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'REGID_3',
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'newSMILES_1',
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'newSMILES_2',
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'newSMILES_3',
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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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'ID',
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'ROMol']].to_csv('thiol_sanitized.csv', index = False)
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