Upload Molecule3D_preprocessing.py

Molecule3D_preprocessing.py

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+# This is a script for Molecule3D dataset preprocessing
+
+# 1. Load modules
+import pandas as pd
+import numpy as np
+import urllib.request
+import tqdm
+import rdkit
+from rdkit import Chem
+import os
+import molvs
+import csv
+import json
+
+standardizer = molvs.Standardizer()
+fragment_remover = molvs.fragment.FragmentRemover()
+
+
+# 2. Download the original dataset
+# Original data
+#  Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs
+#  Zhao Xu, Youzhi Luo, Xuan Zhang, Xinyi Xu, Yaochen Xie, Meng Liu, Kaleb Dickerson, Cheng Deng, Maho Nakata, Shuiwang Ji
+
+# Please download the files from the link below:
+#  https://drive.google.com/drive/u/2/folders/1y-EyoDYMvWZwClc2uvXrM4_hQBtM85BI
+# Suppose the files have been downloaded and unzipped
+
+
+
+# 3. This part adds SMILES in addition to SDF and save CSV files
+#  List of file ranges and corresponding SDF/CSV filenames
+file_ranges = [
+    (0, 1000000),
+    (1000001, 2000000),
+    (2000001, 3000000),
+    (3000001, 3899647)
+]
+
+#  Base directory for input and output files
+base_dir = '/YOUR LOCAL DIRECTORY/' # Please change this part
+
+for start, end in file_ranges:
+    sdf_file = os.path.join(base_dir, f'combined_mols_{start}_to_{end}.sdf')
+    output_csv = os.path.join(base_dir, f'smiles_{start}_{end}.csv')
+
+    # Read the SDF file
+    suppl = Chem.SDMolSupplier(sdf_file)
+
+    # Write to CSV file with SMILES
+    with open(output_csv, mode='w', newline='') as file:
+        writer = csv.writer(file)
+        writer.writerow(['index', 'SMILES'])
+
+        for idx, mol in enumerate(suppl):
+            if mol is None:
+                continue
+
+            smiles = Chem.MolToSmiles(mol)
+            writer.writerow([f'{idx + start + 1}', smiles])   
+            
+          
+''' These files are expected to be stored: 
+smiles_sdf_0_1000000.csv
+smiles_sdf_1000001_2000000.csv
+smiles_sdf_2000001_3000000.csv
+smiles_sdf_3000001_3899647.csv'''
+
+
+
+# 4. Check if there are any missing SMILES or sdf
+
+df1 = pd.read_csv(f'{base_dir}/smiles_sdf_0_1000000.csv')  # Suppose that you have already change the 'base_dir' above
+df2 = pd.read_csv(f'{base_dir}/smiles_sdf_1000001_2000000.csv')
+df3 = pd.read_csv(f'{base_dir}/smiles_sdf_2000001_3000000.csv')
+df4 = pd.read_csv(f'{base_dir}/smiles_sdf_3000001_3899647.csv')
+
+missing_1 = df1[df1.isna().any(axis = 1)]
+missing_2 = df2[df2.isna().any(axis = 1)]
+missing_3 = df3[df3.isna().any(axis = 1)]
+missing_4 = df4[df4.isna().any(axis = 1)]
+
+print('For smiles_sdf_0_1000000.csv file : ', missing_1)
+print('For smiles_sdf_1000001_2000000.csv file : ', missing_2)
+print('For smiles_sdf_2000001_3000000.csv file : ', missing_3)
+print('For smiles_sdf_3000001_3899647.csv file : ', missing_4)
+
+
+
+# 5. Sanitize the molecules with MolVS
+
+# This part would take a few hours 
+df1['X'] = [ \
+    rdkit.Chem.MolToSmiles(
+        fragment_remover.remove(
+        standardizer.standardize(
+        rdkit.Chem.MolFromSmiles(
+        smiles))))
+    for smiles in df1['SMILES']]
+
+problems = []
+for index, row in tqdm.tqdm(df1.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]}")
+
+df1.to_csv('smiles_sdf_0_1000000_sanitized.csv')    
+
+df2['X'] = [ \
+    rdkit.Chem.MolToSmiles(
+        fragment_remover.remove(
+        standardizer.standardize(
+        rdkit.Chem.MolFromSmiles(
+        smiles))))
+    for smiles in df2['SMILES']]
+
+problems = []
+for index, row in tqdm.tqdm(df2.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]}")
+
+df2.to_csv('smiles_sdf_1000001_2000000_sanitized.csv')    
+
+df3['X'] = [ \
+    rdkit.Chem.MolToSmiles(
+        fragment_remover.remove(
+        standardizer.standardize(
+        rdkit.Chem.MolFromSmiles(
+        smiles))))
+    for smiles in df3['SMILES']]
+
+problems = []
+for index, row in tqdm.tqdm(df3.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]}")
+
+df3.to_csv('smiles_sdf_2000001_3000000_sanitized.csv')    
+
+df4['X'] = [ \
+    rdkit.Chem.MolToSmiles(
+        fragment_remover.remove(
+        standardizer.standardize(
+        rdkit.Chem.MolFromSmiles(
+        smiles))))
+    for smiles in df4['SMILES']]
+
+problems = []
+for index, row in tqdm.tqdm(df4.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]}")
+
+df4.to_csv('smiles_sdf_3000001_3899647_sanitized.csv')   
+
+
+
+# 6. Concatenate four sanitized files to one long file
+sanitized1 = pd.read_csv('smiles_sdf_0_1000000_sanitized.csv')
+sanitized2 = pd.read_csv('smiles_sdf_1000001_2000000_sanitized.csv')
+sanitized3 = pd.read_csv('smiles_sdf_2000001_3000000_sanitized.csv')
+sanitized4 = pd.read_csv('smiles_sdf_3000001_3899647_sanitized.csv')
+
+smiles_sdf_concatenated = pd.concat([sanitized1, sanitized2, sanitized3, sanitized4], ignore_index=True)
+
+smiles_sdf_concatenated.to_csv('smiles_sdf_concatenated.csv', index = False) 
+
+
+
+# 7. Combine the properties file to the smiles_sdf_concatenated.csv
+smiles_sdf_concatenated = pd.read_csv('smiles_sdf_concatenated.csv')
+
+properties = pd.read_csv('properties.csv')  # This file is also from the link provided above
+
+smiles_sdf_properties_concatenated = pd.concat([smiles_sdf_concatenated, properties], axis=1)
+
+smiles_sdf_properties_concatenated.to_csv('smiles_sdf_properties.csv', index = False)
+
+
+
+# 8. Rename the columns
+columns_selected = smiles_sdf_properties_concatenated[['Unnamed: 0', 'X', 'sdf', 'cid', 'dipole x', 'dipole y', 'dipole z', 'homo', 'lumo', 'homolumogap', 'scf energy']]
+columns_selected.rename(columns={'Unnamed: 0': 'index', 'X': 'SMILES', 'homolumogap':'Y'}, inplace=True)
+
+columns_selected.to_csv('Molecule3D_final.csv', index=False)
+
+
+
+# 9. Split the dataset by using radom split and scaffold split
+Molecule3D_final = pd.read_csv('Molecule3D_final.csv')
+
+#  Random split
+with open('random_split_inds.json', 'r') as f: # random or scaffold
+    split_data = json.load(f)
+
+random_train = Molecule3D_final[Molecule3D_final['index'].isin(split_data['train'])]
+random_test = Molecule3D_final[Molecule3D_final['index'].isin(split_data['test'])]
+random_valid = Molecule3D_final[Molecule3D_final['index'].isin(split_data['valid'])]
+
+random_train.to_parquet('Molecule3D_random_train.parquet', index=False)
+random_test.to_parquet('Molecule3D_random_test.parquet', index=False)
+random_valid.to_parquet('Molecule3D_random_validation.parquet', index=False)
+
+
+# Scaffold split
+with open('scaffold_split_inds.json', 'r') as f: # random or scaffold
+    split_scaffold = json.load(f)
+
+scaffold_train = Molecule3D_final[Molecule3D_final['index'].isin(split_scaffold['train'])]
+scaffold_test = Molecule3D_final[Molecule3D_final['index'].isin(split_scaffold['test'])]
+scaffold_valid = Molecule3D_final[Molecule3D_final['index'].isin(split_scaffold['valid'])]
+
+scaffold_train.to_parquet('Molecule3D_scaffold_train.parquet', index=False)
+scaffold_test.to_parquet('Molecule3D_scaffold_test.parquet', index=False)
+scaffold_valid.to_parquet('Molecule3D_scaffold_validation.parquet', index=False)
+
+
+
+
+