lipo_datasets.py

"""
Lipophilicity dataset loader.
"""
import os
import deepchem as dc
from deepchem.molnet.load_function.molnet_loader import TransformerGenerator, _MolnetLoader
from deepchem.data import Dataset
from typing import List, Optional, Tuple, Union

LIPO_URL = "https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/Lipophilicity.csv"
LIPO_TASKS = ['exp']


class _LipoLoader(_MolnetLoader):

  def create_dataset(self) -> Dataset:
    dataset_file = os.path.join(self.data_dir, "Lipophilicity.csv")
    if not os.path.exists(dataset_file):
      dc.utils.data_utils.download_url(url=LIPO_URL, dest_dir=self.data_dir)
    loader = dc.data.CSVLoader(
        tasks=self.tasks, feature_field="smiles", featurizer=self.featurizer)
    return loader.create_dataset(dataset_file, shard_size=8192)


def load_lipo(
    featurizer: Union[dc.feat.Featurizer, str] = 'ECFP',
    splitter: Union[dc.splits.Splitter, str, None] = 'scaffold',
    transformers: List[Union[TransformerGenerator, str]] = ['normalization'],
    reload: bool = True,
    data_dir: Optional[str] = None,
    save_dir: Optional[str] = None,
    **kwargs
) -> Tuple[List[str], Tuple[Dataset, ...], List[dc.trans.Transformer]]:
  """Load Lipophilicity dataset

  Lipophilicity is an important feature of drug molecules that affects both
  membrane permeability and solubility. The lipophilicity dataset, curated
  from ChEMBL database, provides experimental results of octanol/water
  distribution coefficient (logD at pH 7.4) of 4200 compounds.

  Scaffold splitting is recommended for this dataset.

  The raw data csv file contains columns below:

  - "smiles" - SMILES representation of the molecular structure
  - "exp" - Measured octanol/water distribution coefficient (logD) of the
    compound, used as label

  Parameters
  ----------
  featurizer: Featurizer or str
    the featurizer to use for processing the data.  Alternatively you can pass
    one of the names from dc.molnet.featurizers as a shortcut.
  splitter: Splitter or str
    the splitter to use for splitting the data into training, validation, and
    test sets.  Alternatively you can pass one of the names from
    dc.molnet.splitters as a shortcut.  If this is None, all the data
    will be included in a single dataset.
  transformers: list of TransformerGenerators or strings
    the Transformers to apply to the data.  Each one is specified by a
    TransformerGenerator or, as a shortcut, one of the names from
    dc.molnet.transformers.
  reload: bool
    if True, the first call for a particular featurizer and splitter will cache
    the datasets to disk, and subsequent calls will reload the cached datasets.
  data_dir: str
    a directory to save the raw data in
  save_dir: str
    a directory to save the dataset in

  References
  ----------
  .. [1] Hersey, A. ChEMBL Deposited Data Set - AZ dataset; 2015.
     https://doi.org/10.6019/chembl3301361
  """
  loader = _LipoLoader(featurizer, splitter, transformers, LIPO_TASKS, data_dir,
                       save_dir, **kwargs)
  return loader.load_dataset('lipo', reload)