# -*- coding: utf-8 -*-
#
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
#
# ClinTox from MoleculeNet for the prediction of clinical trial toxicity
# (or absence of toxicity) and FDA approval status
import pandas as pd
from dgl.data.utils import get_download_dir, download, _get_dgl_url, extract_archive
from .csv_dataset import MoleculeCSVDataset
__all__ = ['ClinTox']
[docs]class ClinTox(MoleculeCSVDataset):
r"""ClinTox from MoleculeNet for the prediction of clinical trial toxicity
(or absence of toxicity) and FDA approval status
Quoting [1], "The ClinTox dataset, introduced as part of this work, 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: (1) clinical
trial toxicity (or absence of toxicity) and (2) 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."
References:
* [1] MoleculeNet: A Benchmark for Molecular Machine Learning.
* [2] DeepChem
Parameters
----------
smiles_to_graph: callable, str -> DGLGraph
A function turning a SMILES string into a DGLGraph. If None, it uses
:func:`dgllife.utils.SMILESToBigraph` by default.
node_featurizer : callable, rdkit.Chem.rdchem.Mol -> dict
Featurization for nodes like atoms in a molecule, which can be used to update
ndata for a DGLGraph. Default to None.
edge_featurizer : callable, rdkit.Chem.rdchem.Mol -> dict
Featurization for edges like bonds in a molecule, which can be used to update
edata for a DGLGraph. Default to None.
load : bool
Whether to load the previously pre-processed dataset or pre-process from scratch.
``load`` should be False when we want to try different graph construction and
featurization methods and need to preprocess from scratch. Default to False.
log_every : bool
Print a message every time ``log_every`` molecules are processed. Default to 1000.
cache_file_path : str
Path to the cached DGLGraphs, default to 'clintox_dglgraph.bin'.
n_jobs : int
The maximum number of concurrently running jobs for graph construction and featurization,
using joblib backend. Default to 1.
Examples
--------
>>> import torch
>>> from dgllife.data import ClinTox
>>> from dgllife.utils import SMILESToBigraph, CanonicalAtomFeaturizer
>>> smiles_to_g = SMILESToBigraph(node_featurizer=CanonicalAtomFeaturizer())
>>> dataset = ClinTox(smiles_to_g)
>>> # Get size of the dataset
>>> len(dataset)
1478
>>> # Get the 0th datapoint, consisting of SMILES, DGLGraph, labels, and masks
>>> dataset[0]
('*C(=O)[C@H](CCCCNC(=O)OCCOC)NC(=O)OCCOC',
Graph(num_nodes=24, num_edges=46,
ndata_schemes={'h': Scheme(shape=(74,), dtype=torch.float32)}
edata_schemes={}),
tensor([1., 0.]),
tensor([1., 1.]))
To address the imbalance between positive and negative samples, we can re-weight
positive samples for each task based on the training datapoints.
>>> train_ids = torch.arange(500)
>>> dataset.task_pos_weights(train_ids)
tensor([ 0.0684, 10.9048])
"""
def __init__(self,
smiles_to_graph=None,
node_featurizer=None,
edge_featurizer=None,
load=False,
log_every=1000,
cache_file_path='./clintox_dglgraph.bin',
n_jobs=1):
self._url = 'dataset/clintox.zip'
data_path = get_download_dir() + '/clintox.zip'
dir_path = get_download_dir() + '/clintox'
download(_get_dgl_url(self._url), path=data_path, overwrite=False)
extract_archive(data_path, dir_path)
df = pd.read_csv(dir_path + '/clintox.csv')
super(ClinTox, self).__init__(df=df,
smiles_to_graph=smiles_to_graph,
node_featurizer=node_featurizer,
edge_featurizer=edge_featurizer,
smiles_column='smiles',
cache_file_path=cache_file_path,
load=load,
log_every=log_every,
init_mask=True,
n_jobs=n_jobs)
[docs] def __getitem__(self, item):
"""Get datapoint with index
Parameters
----------
item : int
Datapoint index
Returns
-------
str
SMILES for the ith datapoint
DGLGraph
DGLGraph for the ith datapoint
Tensor of dtype float32 and shape (T)
Labels of the ith datapoint for all tasks. T for the number of tasks.
Tensor of dtype float32 and shape (T)
Binary masks of the ith datapoint indicating the existence of labels for all tasks.
"""
return self.smiles[item], self.graphs[item], self.labels[item], self.mask[item]