text: """
  With this script you can train one of the 23 included models with your own data
  
  load one of the pre-processed and split datasets that comes with MoleculeACE
  You can find all included datasets here: utils.datasets
  Supported descriptors and ML algorithms can be found here: benchmark.Descriptors, benchmark.Algorithms
  """
  
  from MoleculeACE.benchmark import load_data, models, evaluation, utils, process_data
  import os
  
  # Setup some variables
  dataset = os.path.join(utils.WORKING_DIR, 'benchmark_data', 'CHEMBL287_Ki.csv')
  descriptor = utils.Descriptors.ECFP
  algorithm = utils.Algorithms.GBM
  
  if __name__ == '__main__':
  
      # Process your data data
      process_data(dataset, smiles_colname='smiles', y_colname='exp_mean [nM]', test_size=0.2,
                   fold_threshold=10, similarity_threshold=0.9)
  
      # Load data
      data = load_data(dataset, descriptor=descriptor, tolog10=True)
  
      # Train and optimize a model. You can also implement your own model here
      model = models.train_model(data, algorithm=algorithm, config_file=None)
      predictions = model.test_predict()
  
      # Evaluate your model on activity cliff compounds
      results = evaluation.evaluate(data=data, predictions=predictions)
  