Duplicate from maomlab/MolData

Co-authored-by: Haneul Park <haneulpark@users.noreply.huggingface.co>

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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)
+

README.md

@@ -0,0 +1,178 @@
+---
+version: 1.0.0
+license: cc-by-sa-4.0
+task_categories:
+- tabular-classification
+language:
+- en
+pretty_name: MolData
+size_categories:
+- 1M<n<10M
+tags:
+- drug discovery
+- bioassay
+dataset_summary: A comprehensive disease and target-based dataset with roughly 170
+  million drug screening results from 1.4 million unique molecules and 600 assays
+  which are collected from PubChem to accelerate molecular machine learning for better
+  drug discovery.
+citation: "@article{KeshavarziArshadi2022,\n title = {MolData,  a molecular benchmark\
+  \ for disease and target based machine learning},\n volume = {14},\n ISSN = {1758-2946},\n\
+  \ url = {http://dx.doi.org/10.1186/s13321-022-00590-y},\n DOI = {10.1186/s13321-022-00590-y},\n\
+  \ number = {1},\n journal = {Journal of Cheminformatics},\n publisher = {Springer\
+  \ Science and Business Media LLC},\n author = {Keshavarzi Arshadi,  Arash and Salem,\
+  \  Milad and Firouzbakht,  Arash and Yuan,  Jiann Shiun},\n year = {2022},\n month\
+  \ = mar \n}"
+dataset_info:
+  config_name: MolData
+  features:
+  - name: SMILES
+    dtype: string
+  - name: PUBCHEM_CID
+    dtype: int64
+  - name: PUBCHEM_AID
+    dtype: string
+  - name: Y
+    dtype: int64
+  splits:
+  - name: train
+    num_bytes: 10140424890
+    num_examples: 138547273
+  - name: test
+    num_bytes: 1288513312
+    num_examples: 17069726
+  - name: validation
+    num_bytes: 961758159
+    num_examples: 12728449
+  download_size: 5262157307
+  dataset_size: 12390696361
+configs:
+- config_name: MolData
+  data_files:
+  - split: train
+    path: MolData/train-*
+  - split: test
+    path: MolData/test-*
+  - split: validation
+    path: MolData/validation-*
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: test
+    path: data/test-*
+  - split: validation
+    path: data/validation-*
+---
+
+# MolData
+
+[MolData](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-022-00590-y) is a comprehensive disease and target-based dataset collected from PubChem.
+The dataset contains 1.4 million unique molecules, and it is one the largest efforts to date for democratizing the molecular machine learning.
+This is a mirror of the [Official Github repo](https://github.com/LumosBio/MolData/tree/main/Data) where the dataset was uploaded in 2021.
+
+
+## Preprocessing
+
+We utilized the raw data uploaded on [Github](https://github.com/LumosBio/MolData) and performed several preprocessing:
+1. Sanitize the molecules using RDKit and MolVS (standardize SMILES format)
+2. Formatting (from wide form to long form)
+3. Rename the columns
+4. Split the dataset (train, test, validation)
+
+If you would like to try these processes with the original dataset, 
+please follow the instructions in the [preprocessing script](https://huggingface.co/datasets/maomlab/MolData/blob/main/MolData_preprocessing.py) file located in our MolData repository.
+
+
+
+## Quickstart Usage
+
+### Load a dataset in python
+Each subset can be loaded into python using the Huggingface [datasets](https://huggingface.co/docs/datasets/index) library.
+First, from the command line install the `datasets` library
+
+    $ pip install datasets
+
+then, from within python load the datasets library
+
+    >>> import datasets
+   
+and load the `MolData` datasets, e.g.,
+
+    >>> MolData = datasets.load_dataset("maomlab/MolData", name = "MolData")
+        Generating train split: 100%|███████████████████████████████████████████████████████████████████████████████████████████| 138547273/138547273 [02:07<00:00, 1088043.12 examples/s]
+        Generating test split: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 17069726/17069726 [00:16<00:00, 1037407.67 examples/s]
+        Generating validation split: 100%|████████████████████████████████████████████████████████████████████████████████████████| 12728449/12728449 [00:11<00:00, 1093675.24 examples/s]
+
+and inspecting the loaded dataset
+
+    >>> MolData
+    DatasetDict({
+    train: Dataset({
+        features: ['SMILES', 'PUBCHEM_CID', 'PUBCHEM_AID', 'Y'],
+        num_rows: 138547273
+    })
+    test: Dataset({
+        features: ['SMILES', 'PUBCHEM_CID', 'PUBCHEM_AID', 'Y'],
+        num_rows: 17069726
+    })
+    validation: Dataset({
+        features: ['SMILES', 'PUBCHEM_CID', 'PUBCHEM_AID', 'Y'],
+        num_rows: 12728449
+    })
+})
+
+### Use a dataset to train a model
+One way to use the dataset is through the [MolFlux](https://exscientia.github.io/molflux/) package developed by Exscientia.
+First, from the command line, install `MolFlux` library with `catboost` and `rdkit` support
+
+    pip install 'molflux[catboost,rdkit]'
+
+then load, featurize, split, fit, and evaluate the catboost model
+
+    import json
+    from datasets import load_dataset
+    from molflux.datasets import featurise_dataset
+    from molflux.features import load_from_dicts as load_representations_from_dicts
+    from molflux.splits import load_from_dict as load_split_from_dict
+    from molflux.modelzoo import load_from_dict as load_model_from_dict
+    from molflux.metrics import load_suite
+
+Split and evaluate the catboost model
+    
+    split_dataset = load_dataset('maomlab/MolData', name = 'MolData')
+    
+    split_featurised_dataset = featurise_dataset(
+      split_dataset,
+      column = "SMILES",
+      representations = load_representations_from_dicts([{"name": "morgan"}, {"name": "maccs_rdkit"}]))
+    
+    model = load_model_from_dict({
+        "name": "cat_boost_classifier",
+        "config": {
+            "x_features": ['SMILES::morgan', 'SMILES::maccs_rdkit'],
+            "y_features": ['Y']}})
+    
+    model.train(split_featurised_dataset["train"])
+    preds = model.predict(split_featurised_dataset["test"])
+    
+    classification_suite = load_suite("classification")
+    
+    scores = classification_suite.compute(
+        references=split_featurised_dataset["test"]['Y'],
+        predictions=preds["cat_boost_classifier::Y"])
+
+
+### Citation
+@article{KeshavarziArshadi2022,
+ title = {MolData,  a molecular benchmark for disease and target based machine learning},
+ volume = {14},
+ ISSN = {1758-2946},
+ url = {http://dx.doi.org/10.1186/s13321-022-00590-y},
+ DOI = {10.1186/s13321-022-00590-y},
+ number = {1},
+ journal = {Journal of Cheminformatics},
+ publisher = {Springer Science and Business Media LLC},
+ author = {Keshavarzi Arshadi,  Arash and Salem,  Milad and Firouzbakht,  Arash and Yuan,  Jiann Shiun},
+ year = {2022},
+ month = mar 
+}