Datasets:

maomlab
/

Molecule3D

@@ -1,257 +0,0 @@
-# This is a script for Mole3D dataset preprocessing
-# 1. Load modules
-pip install rdkit
-pip install molvs
-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 sdf files from the link below:
-#  https://drive.google.com/drive/u/2/folders/1y-EyoDYMvWZwClc2uvXrM4_hQBtM85BI
-# And please save the files to your local directory
-# 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 would 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'}, inplace=True)
-columns_selected.to_csv('Molecule3D_final.csv', index=False)
-# 9. Split the dataset by using radom split and scaffold split
-#  Random split
-Molecule3D_final = pd.read_csv('Molecule3D_final.csv')
-with open('random_split_inds.json', 'r') as f: # random or scaffold
-    split_data = json.load(f)
-train_data = Molecule3D_final[Molecule3D_final['index'].isin(split_data['train'])]
-test_data = Molecule3D_final[Molecule3D_final['index'].isin(split_data['test'])]
-valid_data = Molecule3D_final[Molecule3D_final['index'].isin(split_data['valid'])]
-train_data.to_csv('Molecule3D_random_train.csv', index=False)
-test_data.to_csv('Molecule3D_random_test.csv', index=False)
-valid_data.to_csv('Molecule3D_random_validation.csv', index=False)
-print(f"Length of the train dataset: {len(train_data)} rows")
-print(f"Length of the test dataset: {len(test_data)} rows")
-print(f"Length of the valid dataset: {len(valid_data)} rows")
-# 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_csv('Molecule3D_scaffold_train.csv', index=False)
-scaffold_test.to_csv('Molecule3D_scaffold_test.csv', index=False)
-scaffold_valid.to_csv('Molecule3D_scaffold_validation.csv', index=False)
-print(f"Length of the train dataset: {len(scaffold_train)} rows")
-print(f"Length of the test dataset: {len(scaffold_test)} rows")
-print(f"Length of the valid dataset: {len(scaffold_valid)} rows")