#!/usr/bin/python
#-*-coding:utf-8-*-
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Processing of lipohilicity dataset.
Lipophilicity is a dataset curated from ChEMBL database containing experimental results on octanol/water distribution coefficient (logD at pH=7.4).As the Lipophilicity plays an important role in membrane permeability and solubility. Related work deserves more attention.
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
[docs]def get_default_lipophilicity_task_names():
"""Get that default lipophilicity task names and return measured expt"""
return ['exp']
[docs]def load_lipophilicity_dataset(data_path, task_names=None):
"""Load lipophilicity 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
exp: Measured octanol/water distribution coefficient (logD) of the compound, used as label
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_lipophilicity_dataset('./lipophilicity')
print(len(dataset))
References:
[1]Hersey, A. ChEMBL Deposited Data Set - AZ dataset; 2015. https://doi.org/10.6019/chembl3301361
"""
if task_names is None:
task_names = get_default_lipophilicity_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]
data_list = []
for i in range(len(labels)):
data = {
'smiles': smiles_list[i],
'label': labels.values[i],
}
data_list.append(data)
dataset = InMemoryDataset(data_list)
return dataset
[docs]def get_lipophilicity_stat(data_path, task_names):
"""Return mean and std of labels"""
raw_path = join(data_path, 'raw')
csv_file = os.listdir(raw_path)[0]
input_df = pd.read_csv(join(raw_path, csv_file), sep=',')
labels = input_df[task_names].values
return {
'mean': np.mean(labels, 0),
'std': np.std(labels, 0),
'N': len(labels),
}