src/03.sanitize_data.py

#To sanitize the SMILES in the ORD dataset

import pandas as pd
from rdkit import Chem
from molvs import Standardizer

# Read the CSV file
file_path = 'data/Ahneman_ORD_Data.csv'  # replace with your file path
df = pd.read_csv(file_path)

# Initialize the Standardizer from MolVS
standardizer = Standardizer()

# List the columns that contain SMILES strings
smiles_columns = ['inputs["catalyst"].components[0].identifiers[1].value',
                  'inputs["aryl halide"].components[0].identifiers[0].value', 
                  'inputs["base"].components[0].identifiers[1].value', 
                  'inputs["additive"].components[0].identifiers[0].value',
                  'inputs["additive"].components[0].identifiers[1].value', 
                  'outcomes[0].products[0].identifiers[0].value'
                  ]  

def sanitize_smiles(smiles):
    try:
        if pd.isna(smiles):
            return smiles  # Return NA or None if the original data is NA

        mol = Chem.MolFromSmiles(smiles)
        if mol:
            standardized_mol = standardizer.standardize(mol)
            sanitized_smiles = Chem.MolToSmiles(standardized_mol)
            print(f"SMILES successfully sanitized: {sanitized_smiles}")
            return sanitized_smiles
        else:
            return None
    except Exception as e:
        print(f"Error standardizing SMILES: {smiles} -> {e}")
        return None

# Apply sanitization to each SMILES column
for col in smiles_columns:
    df[col] = df[col].apply(sanitize_smiles)

sanitized_df = df
# Save the sanitized SMILES back to a new CSV file
sanitized_df.to_csv('data/Sanitized_Ahneman_ORD_Data.csv', index=False)