## Script to sanitize and split Boldini2024 dataset

#1. Import 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 molvs

standardizer = molvs.Standardizer()
fragment_remover = molvs.fragment.FragmentRemover()


#2. Download the original datasets

# Download the original datasets from the paper
#.  Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery
#.  Davide Boldini, Lukas Friedrich, Daniel Kuhn, and Stephan A. Sieber*
#.  https://github.com/dahvida/AIC_Finder/tree/main/Datasets


#3. Import one of the 17 datasets
#.  Here we chose GPCR.csv for example

df = pd.read_csv("GPCR.csv")


#4. Sanitize with MolVS and print problems

df['X'] = [ \
    rdkit.Chem.MolToSmiles(
        fragment_remover.remove(
        standardizer.standardize(
        rdkit.Chem.MolFromSmiles(
        smiles))))
    for smiles in df['smiles']]

problems = []
for index, row in tqdm.tqdm(df.iterrows()):
    result = molvs.validate_smiles(row['X'])
    if len(result) == 0:
        continue
    problems.append( (row['ID'], result) )

#   Most are because it includes the salt form and/or it is not neutralized
for id, alert in problems:
    print(f"ID: {id}, problem: {alert[0]}")

# Result interpretation
#  - Can't kekulize mol: The error message means that kekulization would break the molecules down, so it couldn't proceed
#  It doesn't mean that the molecules are bad, it just means that normalization failed 


#5. Select columns and rename the dataset

df.rename(columns={'X': 'new SMILES'}, inplace=True)
df[['new SMILES', 'Primary', 'Score', 'Confirmatory']].to_csv('GPCR_sanitized.csv', index=False)