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import os
import pandas as pd
import rdkit
import molvs
import tqdm
import glob
from rdkit import Chem
standardizer = molvs.Standardizer()
fragment_remover = molvs.fragment.FragmentRemover()
excluded_aids = {"584", "585", "1478", "1476", "485294", "485341"} # with/without detergents
file_path = "./active/*.csv"
for file in glob.glob(file_path):
file_name = os.path.splitext(os.path.basename(file))[0]
aid = file_name.replace("pubchem_aid_", "").replace("_active", "")
if aid in excluded_aids:
print(f"Skipping {aid} (excluded).")
continue
output_path = f"./curated/{file_name}_curated.csv"
if os.path.exists(output_path):
print(f"Skipping {file_name}, already exists.")
continue
active_df = pd.read_csv(file)
smiles_series = active_df["CanonicalSMILES"]
active_df["curated_SMILES"] = None
cid = active_df["CID"]
# --------- SMILES sanitization ---------
valid_indices = []
invalid_smiles = []
warning_smiles = []
for idx, smiles in smiles_series.items():
mol = Chem.MolFromSmiles(smiles)
compound_cid = cid.iloc[idx]
if mol is None:
invalid_smiles.append({
'CID': compound_cid,
'SMILES': smiles,
'Reason': "MolFromSmiles returned None"
})
continue
results = molvs.validate_smiles(smiles)
if len(results) > 0:
warning_smiles.append({
'CID': compound_cid,
'SMILES': smiles,
'Reason': results
})
continue
mol = standardizer.standardize(mol)
mol = fragment_remover.remove(mol)
standardized = Chem.MolToSmiles(mol)
active_df.at[idx, "curated_SMILES"] = standardized
valid_indices.append(idx)
# Save valid entries
valid_df = active_df.loc[valid_indices].reset_index(drop=True)
valid_df = valid_df.drop(columns=["CanonicalSMILES"])
valid_df = valid_df.rename(columns={"curated_SMILES": "SMILES"})
# Create DataFrames for invalid and warning
invalid_df = pd.DataFrame(invalid_smiles)
warning_df = pd.DataFrame(warning_smiles)
# Add AID column to dataframes
valid_df.insert(0, "AID", aid)
invalid_df.insert(0, "AID", aid)
warning_df.insert(0, "AID", aid)
# Save csv files
valid_df.to_csv(f'./curated/{file_name}_curated.csv', index=False)
invalid_df.to_csv(f'./curated/{file_name}_invalid_smiles.csv', index=False)
warning_df.to_csv(f'./curated/{file_name}_molvs_validation.csv', index=False)
print(f"Number of compounds in {file_name}:", len(active_df))
print(f"Number of invalid smiles in {file_name}: {len(invalid_df)}")
print(f"Number of warning smiles in {file_name}: {len(warning_df)}\n")
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