/
clintox_datasets.py
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/
clintox_datasets.py
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"""
Clinical Toxicity (clintox) dataset loader.
@author Caleb Geniesse
"""
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
CLINTOX_URL = "https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/clintox.csv.gz"
CLINTOX_TASKS = ['FDA_APPROVED', 'CT_TOX']
class _ClintoxLoader(_MolnetLoader):
def create_dataset(self) -> Dataset:
dataset_file = os.path.join(self.data_dir, "clintox.csv.gz")
if not os.path.exists(dataset_file):
dc.utils.data_utils.download_url(url=CLINTOX_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_clintox(
featurizer: Union[dc.feat.Featurizer, str] = 'ECFP',
splitter: Union[dc.splits.Splitter, str, None] = 'scaffold',
transformers: List[Union[TransformerGenerator, str]] = ['balancing'],
reload: bool = True,
data_dir: Optional[str] = None,
save_dir: Optional[str] = None,
**kwargs
) -> Tuple[List[str], Tuple[Dataset, ...], List[dc.trans.Transformer]]:
"""Load ClinTox dataset
The ClinTox dataset compares drugs approved by the FDA and
drugs that have failed clinical trials for toxicity reasons.
The dataset includes two classification tasks for 1491 drug
compounds with known chemical structures:
#. clinical trial toxicity (or absence of toxicity)
#. FDA approval status.
List of FDA-approved drugs are compiled from the SWEETLEAD
database, and list of drugs that failed clinical trials for
toxicity reasons are compiled from the Aggregate Analysis of
ClinicalTrials.gov(AACT) database.
Random splitting is recommended for this dataset.
The raw data csv file contains columns below:
- "smiles" - SMILES representation of the molecular structure
- "FDA_APPROVED" - FDA approval status
- "CT_TOX" - Clinical trial results
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] Gayvert, Kaitlyn M., Neel S. Madhukar, and Olivier Elemento.
"A data-driven approach to predicting successes and failures of clinical
trials."
Cell chemical biology 23.10 (2016): 1294-1301.
.. [2] Artemov, Artem V., et al. "Integrated deep learned transcriptomic and
structure-based predictor of clinical trials outcomes." bioRxiv (2016):
095653.
.. [3] Novick, Paul A., et al. "SWEETLEAD: an in silico database of approved
drugs, regulated chemicals, and herbal isolates for computer-aided drug
discovery." PloS one 8.11 (2013): e79568.
.. [4] Aggregate Analysis of ClincalTrials.gov (AACT) Database.
https://www.ctti-clinicaltrials.org/aact-database
"""
loader = _ClintoxLoader(featurizer, splitter, transformers, CLINTOX_TASKS,
data_dir, save_dir, **kwargs)
return loader.load_dataset('clintox', reload)