#!/usr/bin/python
#-*-coding:utf-8-*-
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"""
Processing of muv dataset.
The Maximum Unbiased Validation (MUV) group is a benchmark dataset selected from PubChem BioAssay by applying a refined nearest neighbor analysis. The MUV dataset contains 17 challenging tasks for around 90,000 compounds and is specifically designed for validation of virtual screening techniques.
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_muv_task_names', 'load_muv_dataset']
[docs]def get_default_muv_task_names():
"""Get that default hiv task names and return the measured results for bioassays"""
return ['MUV-466', 'MUV-548', 'MUV-600', 'MUV-644', 'MUV-652', 'MUV-689',
'MUV-692', 'MUV-712', 'MUV-713', 'MUV-733', 'MUV-737', 'MUV-810',
'MUV-832', 'MUV-846', 'MUV-852', 'MUV-858', 'MUV-859']
[docs]def load_muv_dataset(data_path, task_names=None):
"""Load muv 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.
mol_id: PubChem CID of the compound.
MUV-XXX: Measured results (Active/Inactive) for bioassays.
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_muv_dataset('./muv')
print(len(dataset))
References:
[1]Rohrer, Sebastian G., and Knut Baumann. “Maximum unbiased validation (MUV) data sets for virtual screening based on PubChem bioactivity data.” Journal of chemical information and modeling 49.2 (2009): 169-184.
"""
if task_names is None:
task_names = get_default_muv_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