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# -*- coding: utf-8 -*-
# The B3clf library computes the blood-brain barrier (BBB) permeability
# of organic molecules with resampling strategies.
#
# Copyright (C) 2021 The Ayers Lab
#
# This file is part of B3clf.
#
# B3clf is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 3
# of the License, or (at your option) any later version.
#
# B3clf is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, see <http://www.gnu.org/licenses/>
#
# --
import os
import sys
cwd = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.join(cwd, "padelpy"))
import pandas as pd
from rdkit import Chem
from padelpy import from_sdf
"""Compute PaDEL descriptors."""
def compute_descriptors(sdf_file,
# Change this to be an optional argument
excel_out="padel_descriptors.xlsx",
output_csv=None,
timeout=None,
time_per_molecule=-1,
) -> pd.DataFrame:
"""Compute the chemical descriptors with PaDEL.
Parameters
----------
sdf_file : str
Input SDF file name.
excel_out : str, optional
Excel file name to save PaDEL descriptors.
timeout : float
The maximum time, in seconds, for calculating the descriptors. When set to be None,
this does not take effect.
Returns
-------
df_desc : pandas.dataframe
The computed pandas dataframe of PaDEL descriptors.
"""
desc = from_sdf(sdf_file=sdf_file,
output_csv=output_csv,
descriptors=True,
fingerprints=False,
timeout=timeout,
maxruntime=time_per_molecule,
)
df_desc = pd.DataFrame(desc)
# add molecule names to dataframe
suppl = Chem.SDMolSupplier(sdf_file,
sanitize=True,
removeHs=False,
strictParsing=True)
mol_names = [mol.GetProp("_Name") for mol in suppl]
df_desc.index = mol_names
df_desc.index.name = "ID"
# drop rows with nan values
# todo: add imputation option
df_desc.dropna(axis=0, inplace=True)
# save results
if excel_out is not None:
df_desc.to_excel(excel_out, engine="openpyxl")
return df_desc
# Index will be the molecule's name
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