#!/usr/bin/python
#-*-coding:utf-8-*-
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"""
Processing of Blood-Brain Barrier Penetration dataset
The Blood-brain barrier penetration (BBBP) dataset is extracted from a study on the modeling and
prediction of the barrier permeability. As a membrane separating circulating blood and brain extracellular fluid, the blood-brain barrier blocks most drugs, hormones and neurotransmitters. Thus penetration of the barrier forms a long-standing issue in development of drugs targeting central nervous system.
This dataset includes binary labels for over 2000 compounds on their permeability properties.
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_bbbp_task_names', 'load_bbbp_dataset']
[docs]def get_default_bbbp_task_names():
"""Get that default bbbp task names and return the binary labels"""
return ['p_np']
[docs]def load_bbbp_dataset(data_path, task_names=None):
"""Load bbbp dataset ,process the classification labels and the input information.
Description:
The data file contains a csv table, in which columns below are used:
Num:number
name:Name of the compound
smiles:SMILES representation of the molecular structure
p_np:Binary labels for penetration/non-penetration
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_bbbp_dataset('./bbbp')
print(len(dataset))
References:
[1] Martins, Ines Filipa, et al. “A Bayesian approach to in silico blood-brain barrier penetration modeling.” Journal of chemical information and modeling 52.6 (2012): 1686-1697.
"""
if task_names is None:
task_names = get_default_bbbp_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