preprocessing scripts/Nano Luciferase Interference_preprocessing script.py

# 1. Load 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 = 'Nano_Luciferase_counter_assay_training_set_curated.sdf'
dataframe = PandasTools.LoadSDF(sdfFile)
dataframe.to_csv('Nano_Luciferase.csv', index=False)
df = pd.read_csv('Nano_Luciferase.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(2, 'REGID_3', np.NaN)

df['REGID_3'] = df['REGID_2'].str.split(',').str[1]
df['REGID_2'] = df['REGID_2'].str.split(',').str[0]

df.insert(4, 'SMILES_2', np.NaN)
df.insert(5, 'SMILES_3', np.NaN)

df[['Raw_SMILES', 'SMILES_2', 'SMILES_3']] = df['Raw_SMILES'].str.split(';', expand=True)

df.rename(columns= {'Raw_SMILES' : 'SMILES_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)

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_3'] = df['SMILES_3'].apply(process_smiles)


# 5. Rename the columns

df.rename(columns={'X_1' : 'newSMILES_1', 'X_2' : 'newSMILES_2', 'X_3' : 'newSMILES_3'}, inplace = True)

# 6. Create a file with sanitized SMILES

df[['REGID_1',
 'REGID_2',
 'REGID_3',
 'newSMILES_1',
 'newSMILES_2',
 'newSMILES_3',
 'log_AC50_M',
 'Efficacy',
 'CC-v2',
 'Outcome',
 'InChIKey',
 'ID',
 'ROMol']].to_csv('Nano Luciferase_sanitized.csv', index = False)