Datasets:

maomlab
/

MolData

# 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)

	# 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)