chembl into benchmark

from delaney.delaney_datasets import load_delaney

from nci.nci_datasets import load_nci

from pdbbind.pdbbind_datasets import load_pdbbind_grid

from chembl.chembl_datasets import load_chembl

def benchmark_loading_datasets(hyper_parameters, 

                               dataset='tox21', model='tf', split=None,

  if dataset in ['muv', 'pcba', 'tox21', 'sider', 'toxcast']:

    mode = 'classification'

  elif dataset in ['kaggle', 'delaney', 'nci','pdbbind']:

  elif dataset in ['kaggle', 'delaney', 'nci', 'pdbbind', 'chembl']:

    mode = 'regression'

  else:

    raise ValueError('Dataset not supported')

                       'pcba': load_pcba, 'nci': load_nci,

                       'sider': load_sider, 'toxcast': load_toxcast,

                       'kaggle': load_kaggle, 'delaney': load_delaney,

                       'pdbbind': load_pdbbind_grid}

                       'pdbbind': load_pdbbind_grid,

                       'chembl': load_chembl}

  print('-------------------------------------')

  print('Benchmark %s on dataset: %s' % (model, dataset))

              'tf_regression', 'graphconvreg']

  if len(datasets) == 0:

    datasets = ['tox21', 'sider', 'muv', 'toxcast', 'pcba', 

                'delaney', 'nci', 'kaggle', 'pdbbind']

                'delaney', 'nci', 'kaggle', 'pdbbind', 'chembl']

  #input hyperparameters

  #tf: dropouts, learning rate, layer_sizes, weight initial stddev,penalty,