#!/usr/bin/python
#-*-coding:utf-8-*-
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"""
Processing of toxcast dataset.
ToxCast is an extended data collection from the same initiative as Tox21, providing toxicology data for a large library of compounds based on in vitro high-throughput screening. The processed collection includes qualitative results of over 600 experiments on 8k compounds.
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_toxcast_task_names', 'load_toxcast_dataset']
[docs]def get_default_toxcast_task_names(data_path):
"""Get that default toxcast task names and return the list of the input information"""
raw_path = join(data_path, 'raw')
csv_file = os.listdir(raw_path)[0]
input_df = pd.read_csv(join(raw_path, csv_file), sep=',')
return list(input_df.columns)[1:]
[docs]def load_toxcast_dataset(data_path, task_names=None):
"""Load toxcast 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.
ACEA_T47D_80hr_Negative: “Tanguay_ZF_120hpf_YSE_up” - 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_toxcast_dataset('./toxcast')
print(len(dataset))
References:
[1]Richard, Ann M., et al. “ToxCast chemical landscape: paving the road to 21st century toxicology.” Chemical research in toxicology 29.8 (2016): 1225-1251.
[2]please refer to the section “high-throughput assay information” at https://www.epa.gov/chemical-research/toxicity-forecaster-toxcasttm-data for details.
"""
if task_names is None:
task_names = get_default_toxcast_task_names(data_path)
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]
# Some smiles could not be successfully converted
# to rdkit mol object so them to None
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]
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)):
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