#!/usr/bin/python
#-*-coding:utf-8-*-
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"""
Processing of tox21 dataset.
The “Toxicology in the 21st Century” (Tox21) initiative created a public database measuring toxicity of compounds, which has been used in the 2014 Tox21 Data Challenge. This dataset contains qualitative toxicity measurements for 8k compounds on 12 different targets, including nuclear receptors and stress response pathways.
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_tox21_task_names', 'load_tox21_dataset']
[docs]def get_default_tox21_task_names():
"""Get that default tox21 task names and return the bioassays results"""
return ['NR-AR', 'NR-AR-LBD', 'NR-AhR', 'NR-Aromatase', 'NR-ER', 'NR-ER-LBD',
'NR-PPAR-gamma', 'SR-ARE', 'SR-ATAD5', 'SR-HSE', 'SR-MMP', 'SR-p53']
[docs]def load_tox21_dataset(data_path, task_names=None):
"""Load tox21 dataset,process the input information.
Description:
The data file contains a csv table, in which columns below are used:
smiles: SMILES representation of the molecular structure.
NR-XXX: Nuclear receptor signaling bioassays results.
SR-XXX: Stress response bioassays 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_tox21_dataset('./tox21')
print(len(dataset))
References:
[1]Tox21 Challenge. https://tripod.nih.gov/tox21/challenge/
[2]please refer to the links at https://tripod.nih.gov/tox21/challenge/data.jsp for details.
"""
if task_names is None:
task_names = get_default_tox21_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']
labels = input_df[task_names]
labels = labels.replace(0, -1) # convert 0 to -1
labels = labels.fillna(0) # convert nan to 0
data_list = []
for i in range(len(smiles_list)):
data = {}
data['smiles'] = smiles_list[i]
data['label'] = labels.values[i]
data_list.append(data)
dataset = InMemoryDataset(data_list)
return dataset