Upload MolData_preprocessing.py

MolData_preprocessing.py

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+# 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)
+