# This is a script for MolData dataset preprocessing # 1. Load modules import pandas as pd import numpy as np import urllib.request import rdkit from rdkit import Chem import os import molvs import csv import json import tqdm standardizer = molvs.Standardizer() fragment_remover = molvs.fragment.FragmentRemover() # 2. Download the original dataset # https://github.com/LumosBio/MolData # Suppose that 'all_molecular_data.csv' has been downloaded from GitHub # 3. Check if any SMILES is missing in the dataset (first column) df = pd.read_csv('all_molecular_data.csv') missing_SMILES = df[df.iloc[:, 0].isna()] print(f'There are {len(missing_SMILES)} rows with missing SMILES.') # This prints 'There are 0 rows with missing SMILES.' # 4. Sanitize SMILES with MolVS and print problems # Since the dataset is large, we divided it into four portions to sanitize quarter_df_1 = df.iloc[:len(df)//4] quarter_df_1['X'] = [ \ rdkit.Chem.MolToSmiles( fragment_remover.remove( standardizer.standardize( rdkit.Chem.MolFromSmiles( smiles)))) for smiles in quarter_df_1['smiles']] problems = [] for index, row in tqdm.tqdm(quarter_df_1.iterrows()): result = molvs.validate_smiles(row['X']) if len(result) == 0: continue problems.append((row['X'], result)) # Most are because it includes the salt form and/or it is not neutralized for result, alert in problems: print(f"SMILES: {result}, problem: {alert[0]}") quarter_df_1.to_csv('MolData_sanitized_0.25.csv') quarter_df_2 = df.iloc[len(df)//4 : len(df)//2] quarter_df_2['X'] = [ \ rdkit.Chem.MolToSmiles( fragment_remover.remove( standardizer.standardize( rdkit.Chem.MolFromSmiles( smiles)))) for smiles in quarter_df_2['smiles']] problems = [] for index, row in tqdm.tqdm(quarter_df_2.iterrows()): result = molvs.validate_smiles(row['X']) if len(result) == 0: continue problems.append((row['X'], result)) # Most are because it includes the salt form and/or it is not neutralized for result, alert in problems: print(f"SMILES: {result}, problem: {alert[0]}") quarter_df_2.to_csv('MolData_sanitized_0.5.csv') quarter_df_3 = df.iloc[len(df)//2 : 3 *len(df)//4] quarter_df_3['X'] = [ \ rdkit.Chem.MolToSmiles( fragment_remover.remove( standardizer.standardize( rdkit.Chem.MolFromSmiles( smiles)))) for smiles in quarter_df_3['smiles']] problems = [] for index, row in tqdm.tqdm(quarter_df_3.iterrows()): result = molvs.validate_smiles(row['X']) if len(result) == 0: continue problems.append((row['X'], result)) # Most are because it includes the salt form and/or it is not neutralized for result, alert in problems: print(f"SMILES: {result}, problem: {alert[0]}") quarter_df_3.to_csv('MolData_sanitized_0.75.csv') quarter_df_4 = df.iloc[3 *len(df)//4 :len(df)] quarter_df_4['X'] = [ \ rdkit.Chem.MolToSmiles( fragment_remover.remove( standardizer.standardize( rdkit.Chem.MolFromSmiles( smiles)))) for smiles in quarter_df_4['smiles']] problems = [] for index, row in tqdm.tqdm(quarter_df_4.iterrows()): result = molvs.validate_smiles(row['X']) if len(result) == 0: continue problems.append((row['X'], result)) # Most are because it includes the salt form and/or it is not neutralized for result, alert in problems: print(f"SMILES: {result}, problem: {alert[0]}") quarter_df_4.to_csv('MolData_sanitized_1.0.csv') # 4. Concatenate sanitized1 = pd.read_csv('MolData_sanitized_0.25.csv') sanitized2 = pd.read_csv('MolData_sanitized_0.5.csv') sanitized3 = pd.read_csv('MolData_sanitized_0.75.csv') sanitized4 = pd.read_csv('MolData_sanitized_1.0.csv') smiles_concatenated = pd.concat([sanitized1, sanitized2, sanitized3, sanitized4], ignore_index=True) smiles_concatenated.to_csv('MolData_sanitized_concatenated.csv', index = False) # 5. Formatting and naming (wide form to long form, & column naming) # Due to the large size of the dataset, we processed it using chunks to efficiently handle the data. chunk_size = 10**5 input_file = 'MolData_sanitized_concatenated.csv' output_prefix = 'MolData_long_form_' column_names = pd.read_csv(input_file, nrows=1).columns column_names = column_names.tolist() column_names = ['SMILES' if col == 'X' else col for col in column_names] var_name_list = [col for col in column_names if col.startswith('activity_')] with pd.read_csv(input_file, chunksize=chunk_size) as reader: for i, chunk in enumerate(reader): chunk.columns = column_names long_df = pd.melt(chunk, id_vars=['SMILES', 'PUBCHEM_CID', 'split'], value_vars=var_name_list, var_name='AID', value_name='score') long_df = long_df.dropna(subset=['score']) long_df['score'] = long_df['score'].astype('Int64') output_file = f"{output_prefix}{i+1}.csv" long_df.to_csv(output_file, index=False) print(f"Saved: {output_file}") # 6. Split into train, test, and validation chunk_size = 10**5 input_files = [f'MolData_long_form_{i+1}.csv' for i in range(15)] output_train_file = 'MolData_train.csv' output_test_file = 'MolData_test.csv' output_valid_file = 'MolData_validation.csv' train_data = [] test_data = [] valid_data = [] for input_file in input_files: with pd.read_csv(input_file, chunksize=chunk_size) as reader: for chunk in reader: train_chunk = chunk[chunk['split'] == 'train'] test_chunk = chunk[chunk['split'] == 'test'] valid_chunk = chunk[chunk['split'] == 'validation'] train_data.append(train_chunk) test_data.append(test_chunk) valid_data.append(valid_chunk) train_df = pd.concat(train_data, ignore_index=True) test_df = pd.concat(test_data, ignore_index=True) valid_df = pd.concat(valid_data, ignore_index=True) train_df.to_csv(output_train_file, index=False) test_df.to_csv(output_test_file, index=False) valid_df.to_csv(output_valid_file, index=False) def fix_cid_column(df): df['PUBCHEM_CID'] = df['PUBCHEM_CID'].astype(str).apply(lambda x: x.split(',')[0]) # Because some molecule have two CIDs df['PUBCHEM_CID'] = df['PUBCHEM_CID'].astype('Int64') df = df.rename(columns = {'score' : 'Y'}) # This is for column renaming return df train_csv = fix_cid_column(pd.read_csv('MolData_train.csv')) test_csv = fix_cid_column(pd.read_csv('MolData_test.csv')) valid_csv = fix_cid_column(pd.read_csv('MolData_validation.csv')) train_csv.to_parquet('MolData_train.parquet', index=False) test_csv.to_parquet('MolData_test.parquet', index=False) valid_csv.to_parquet('MolData_validation.parquet', index=False)