#!/usr/bin/python
#-*-coding:utf-8-*-
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"""
Processing of 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: (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.
You can download the dataset from
http://moleculenet.ai/datasets-1 and load it into pahelix reader creators
"""
import os
from os.path import join, exists
import pandas as pd
import numpy as np
from pahelix.datasets.inmemory_dataset import InMemoryDataset
__all__ = ['get_default_clintox_task_names', 'load_clintox_dataset']
[docs]def get_default_clintox_task_names():
"""Get that default clintox task names and return class"""
return ['FDA_APPROVED', 'CT_TOX']
[docs]def load_clintox_dataset(data_path, task_names=None):
"""Load Clintox dataset ,process the classification labels and the input information.
Description:
The data file contains a csv table, in which columns below are used:
smiles: SMILES representation of the molecular structure
FDA_APPROVED: FDA approval status
CT_TOX: Clinical trial results
Args:
data_path(str): the path to the cached npz path.
task_names(list): a list of header names to specify the columns to fetch from
the csv file.
Returns:
an InMemoryDataset instance.
Example:
.. code-block:: python
dataset = load_clintox_dataset('./clintox')
print(len(dataset))
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
"""
if task_names is None:
task_names = get_default_clintox_task_names()
raw_path = join(data_path, 'raw')
csv_file = os.listdir(raw_path)[0]
input_df = pd.read_csv(join(raw_path, csv_file), sep=',')
smiles_list = input_df['smiles']
from rdkit.Chem import AllChem
rdkit_mol_objs_list = [AllChem.MolFromSmiles(s) for s in smiles_list]
preprocessed_rdkit_mol_objs_list = [m if not m is None else None
for m in rdkit_mol_objs_list]
smiles_list = [AllChem.MolToSmiles(m) if not m is None else None
for m in preprocessed_rdkit_mol_objs_list]
labels = input_df[task_names]
# convert 0 to -1
labels = labels.replace(0, -1)
# there are no nans
data_list = []
for i in range(len(smiles_list)):
if smiles_list[i] is None:
continue
data = {}
data['smiles'] = smiles_list[i]
data['label'] = labels.values[i]
data_list.append(data)
dataset = InMemoryDataset(data_list)
return dataset