removed openMP parallelization as option

from pathos.multiprocessing import ProcessingPool

import multiprocessing as mp

from functools import partial

import multiprocess

import dill

def generate_scaffold(smiles, include_chirality=False):

  """Compute the Bemis-Murcko scaffold for a SMILES string."""

      molecule_features = featurizer.featurize_complexes([ligand_pdb], [protein_pdb])

      return molecule_features

    if not parallel:

    if worker_pool is None:

      features = []

      for ligand_protein_pdb_tuple in zip(ligand_pdbs, protein_pdbs):

        features.append(featurize_wrapper(ligand_protein_pdb_tuple))

    """

    sample_smiles = df["smiles"].tolist()

    if not parallel:

    if worker_pool is None:

      features = []

      for ind, smiles in enumerate(sample_smiles):

        if ind % self.log_every_n == 0:

        mol = Chem.MolFromSmiles(smiles)

        features.append(featurizer.featurize([mol]))

    else:

      def featurize_wrapper(smiles):

      def featurize_wrapper(smiles, dilled_featurizer):

      	print("Featurizing %s" % smiles)

        mol = Chem.MolFromSmiles(smiles)

        return featurizer.featurize([mol])

        featurizer = dill.loads(dilled_featurizer)

        feature = featurizer.featurize([mol])

        return feature

      if worker_pool is None:

        dilled_featurizer = dill.dumps(featurizer)

        worker_pool = ProcessingPool(mp.cpu_count())

        features = worker_pool.map(featurize_wrapper, 

                                   sample_smiles)

        featurize_wrapper_partial = partial(featurize_wrapper,

                                            dilled_featurizer=dilled_featurizer)

        features = []

        for smiles in sample_smiles:

          features.append(featurize_wrapper_partial(smiles))

      else:

        features = worker_pool.map_sync(featurize_wrapper, 

                                        sample_smiles)