# from syba.syba import SybaClassifier

# def SYBAscore(smiles_list):
#     """
#     Compute the average SYBA score for a list of SMILES strings.

#     Parameters:
#     - smiles_list (list of str): A list of SMILES strings representing molecules.

#     Returns:
#     - float: The average SYBA score for the list of molecules.
#     """
#     syba = SybaClassifier()
#     syba.fitDefaultScore()
#     scores = []

#     for smiles in smiles_list:
#         try:
#             score = syba.predict(smi=smiles)
#             scores.append(score)
#         except Exception as e:
#             print(f"Error processing SMILES '{smiles}': {e}")
#             continue

#     if scores:
#         return sum(scores) / len(scores)
#     else:
#         return None  # Or handle empty list or all failed predictions as needed


# syba = SybaClassifier()
# syba.fitDefaultScore()
# smi = "O=C(C)Oc1ccccc1C(=O)O"
# print(syba.predict(smi))

import sascorer