Synced repo using 'sync_with_huggingface' Github Action

.pre-commit-config.yaml

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+minimum_pre_commit_version: 3.4.0
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.4.0
+    hooks:
+      - id: check-merge-conflict  # Check for files that contain merge conflict strings.
+      - id: trailing-whitespace   # Trims trailing whitespace.
+        args: [--markdown-linebreak-ext=md]
+      - id: mixed-line-ending     # Replaces or checks mixed line ending.
+        args: [--fix=lf]
+      - id: end-of-file-fixer     # Makes sure files end in a newline and only a newline.
+      - id: check-merge-conflict  # Check for files that contain merge conflict strings.
+      - id: check-ast             # Simply check whether files parse as valid python.

LICENSE

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
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+  15. Disclaimer of Warranty.
+
+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
+OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    {one line to give the program's name and a brief idea of what it does.}
+    Copyright (C) {year}  {name of author}
+
+    This program 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.
+
+    This program 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/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    {project}  Copyright (C) {year}  {fullname}
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<http://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<http://www.gnu.org/philosophy/why-not-lgpl.html>.

MANIFEST.in

@@ -0,0 +1,3 @@
+include b3clf/pre_trained/*
+include b3clf/feature_list.txt
+include b3clf/data/B3clf_thresholds.xlsx

b3clf/__init__.py

@@ -0,0 +1,31 @@
+# -*- 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/>
+#
+# --
+
+"""Package for BBB predictions."""
+
+try:
+    from .version import __version__
+except ImportError:
+    __version__ = "0.0.0.post0"
+
+from .b3clf import b3clf

b3clf/__main__.py

@@ -0,0 +1,109 @@
+# -*- 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/>
+#
+# --
+
+"""Package for BBB predictions."""
+import argparse
+
+from .b3clf import b3clf
+
+try:
+    from .version import __version__
+except ImportError:
+    __version__ = "0.0.0.post0"
+
+
+def main():
+    # https://docs.python.org/3/library/argparse.html
+    parser = argparse.ArgumentParser(
+        description="b3clf predicts if molecules can pass blood-brain barrier with resampling "
+                    "strategies.",
+    )
+    parser.add_argument("-mol",
+                        default="input.sdf",
+                        type=str,
+                        help="Input file with descriptors.")
+    parser.add_argument("-sep",
+                        type=str,
+                        default="\s+|\t+",
+                        help="""Separator for input file. Default="\s+|\\t+".""")
+    parser.add_argument("-clf",
+                        type=str,
+                        default="xgb",
+                        help="Classification algorithm type. Default=xgb.")
+    parser.add_argument("-sampling",
+                        type=str,
+                        default="classic_ADASYN",
+                        help="Resampling method type. Default=classic_ADASYN.")
+    parser.add_argument("-output",
+                        type=str,
+                        default="B3clf_output.xlsx",
+                        help="Name of output file, CSV or XLSX format. Default=B3clf_output.xlsx.")
+    parser.add_argument("-verbose",
+                        type=int,
+                        default=1,
+                        help="If verbose is not zero, B3clf will print out the predictions. "
+                             "Default=1.")
+    parser.add_argument("-random_seed",
+                        type=int,
+                        default=42,
+                        help="""Romdom seed to control randonness. If set to be "None", """
+                             """it will result in a randomness of the predictions. Default=42.""")
+    parser.add_argument("-time_per_mol",
+                        type=int,
+                        default=-1,
+                        help="""Time per molecule in seconds. If set to be -1, no time limit. """
+                        """Default=-1.""")
+    parser.add_argument("-keep_features",
+                        type=str,
+                        default="no",
+                        help="""To keep computed feature file ("yes") or not ("no"). Default=no.""")
+    parser.add_argument("-keep_sdf",
+                        type=str,
+                        default="no",
+                        help="""To keep computed molecular geometries ("yes") or not ("no"). Default=no.""")
+    parser.add_argument("-threshold",
+                        type=str,
+                        default="none",
+                        help="""Threshold used for the classification which can be "none", """
+                             """"J_threshold" and "F_threshold". "J_threshold" will use """
+                             """threshold optimized from Youden’s J statistic. "F_threshold" will """
+                             """use threshold optimized from F score. Default="none".""")
+    args = parser.parse_args()
+
+    _ = b3clf(mol_in=args.mol,
+              sep=args.sep,
+              clf=args.clf,
+              sampling=args.sampling,
+              output=args.output,
+              verbose=args.verbose,
+              random_seed=args.random_seed,
+              time_per_mol=args.time_per_mol,
+              keep_features=args.keep_features,
+              keep_sdf=args.keep_sdf,
+              threshold=args.threshold,
+              )
+
+
+if __name__ == "__main__":
+    """B3clf command-line interface."""
+    main()

b3clf/b3clf.py

@@ -0,0 +1,154 @@
+# -*- 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/>
+#
+# --
+
+"""
+Main B3clf Script.
+"""
+
+# Todo: Enable b3clf prediction without PaDeL calculation from PaDeL descriptor input
+import os
+
+import numpy as np
+from .descriptor_padel import compute_descriptors
+from .geometry_opt import geometry_optimize
+from .utils import (get_descriptors, predict_permeability,
+                    scale_descriptors, select_descriptors)
+
+__all__ = [
+    "b3clf",
+]
+
+
+def b3clf(mol_in,
+          sep="\s+|\t+",
+          clf="xgb",
+          sampling="classic_ADASYN",
+          output="B3clf_output.xlsx",
+          verbose=1,
+          random_seed=42,
+          time_per_mol=-1,
+          keep_features="no",
+          keep_sdf="no",
+          threshold="none",
+          ):
+    """Use B3clf for BBB classifications with resampling strategies.
+
+    Parameters
+    ----------
+    mol_in : str
+        Input molecule text fie which can be SMILES strings (file extension with .smi or .csv) or
+        SDF file format. No space is allowed for molecular name if input is a file with SMILES strings.
+    sep : str, optional
+        Separator used to parse data if a text file with SMILES strings is provided.
+        Default="\s+|\t+" which will take any space and any tab as delimiter.
+    clf: str, optional
+        Classification algorithm, which can be "dtree" for decision trees, "knn" for kNN, "logreg"
+        for logistical regression and "xgb" for XGBoost. Default="xgb".
+    sampling : str, optional
+        Sampling strategies that can be used which includes "common",
+        "RandUndersampling", "SMOTE", "borderline_SMOTE", "kmeans_SMOTE" and "classic_ADASYN". The
+        "common" denotes that no resampling strategy is employed. Default="classic_ADASYN".
+    output : str, optional
+        Output file name for the predicted results consisting molecule ID, predicted probability
+        and labels for BBB permeability.
+    verbose : int, optional
+        When verbose is zero, no results are printed out. Otherwise, the program prints the
+        predictions. Default=1.
+    random_seed : int, optional
+        Random seed for reproducibility. Default=42.
+    time_per_mol : int, optional
+        Time limit for each molecule in seconds. Default=-1, which means no time limit.
+    keep_features : str, optional
+        To keep intermediate molecular feature file, "yes" or "no". Default="no".
+    keep_sdf : str, optional
+        To keep intermediate molecular geometry file with 3D coordinates, "yes" or "no".
+        Default="no".
+    threshold : str, optional
+        To set the threshold for the predicted probability which can be "none". "J_threshold" and
+        "F_threshold". "J_threshold" will use threshold optimized from Youden’s J statistic.
+        "F_threshold" will use threshold optimized from F score. Default="none".
+
+    Returns
+    -------
+    result_df : pandas.DataFrame
+        Result of BBB predictions with molecule ID/name, predicted probability and predicted labels.
+
+    """
+
+    # set random seed
+    if random_seed is not None:
+        rng = np.random.default_rng(random_seed)
+
+    mol_tag = os.path.basename(mol_in).split(".")[0]
+
+    features_out = f"{mol_tag}_padel_descriptors.xlsx"
+    internal_sdf = f"{mol_tag}_optimized_3d.sdf"
+
+    # Geometry optimization
+    # Input:
+    # * Either an SDF file with molecular geometries or a text file with SMILES strings
+
+    geometry_optimize(input_fname=mol_in, output_sdf=internal_sdf, sep=sep)
+
+    _ = compute_descriptors(sdf_file=internal_sdf,
+                            excel_out=features_out,
+                            output_csv=None,
+                            timeout=None,
+                            time_per_molecule=time_per_mol,
+                            )
+
+    # Get computed descriptors
+    X_features, info_df = get_descriptors(df=features_out)
+    # X_features, info_df = get_descriptors(internal_df)
+
+    # Select descriptors
+    X_features = select_descriptors(df=X_features)
+
+    # Scale descriptors
+    X_features = scale_descriptors(df=X_features)
+
+    # Get classifier
+    # clf = get_clf(clf_str=clf, sampling_str=sampling)
+
+    # Get classifier
+    result_df = predict_permeability(clf_str=clf,
+                                     sampling_str=sampling,
+                                     features_df=X_features,
+                                     info_df=info_df,
+                                     threshold=threshold)
+
+    # Get classifier
+    display_cols = ["ID", "SMILES", "B3clf_predicted_probability", "B3clf_predicted_label"]
+
+    result_df = result_df[[col for col in result_df.columns.to_list() if col in display_cols]]
+    if verbose != 0:
+        print(result_df)
+
+    result_df.to_excel(output, index=None, engine="openpyxl")
+
+    if keep_features != "yes":
+        os.remove(features_out)
+    if keep_sdf != "yes":
+        os.remove(internal_sdf)
+
+    return result_df

b3clf/descriptor_padel.py

@@ -0,0 +1,90 @@
+# -*- 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

b3clf/feature_list.txt

@@ -0,0 +1,475 @@
+nAcid
+ALogP
+ALogp2
+AMR
+naAromAtom
+nH
+nN
+nO
+nS
+nP
+nF
+nCl
+nBr
+nI
+nX
+ATS0m
+ATS4m
+ATS2s
+AATS0m
+AATS1m
+AATS4m
+AATS5m
+AATS6m
+AATS7m
+AATS8m
+AATS0v
+AATS3v
+AATS5v
+AATS7v
+AATS0e
+AATS4e
+AATS5e
+AATS6e
+AATS7e
+AATS4p
+AATS0i
+AATS1i
+AATS4i
+AATS5i
+AATS8i
+AATS4s
+AATS5s
+AATS6s
+AATS7s
+AATS8s
+ATSC2c
+ATSC3c
+ATSC4c
+ATSC5c
+ATSC6c
+ATSC7c
+ATSC8c
+ATSC1m
+ATSC2m
+ATSC3m
+ATSC4m
+ATSC5m
+ATSC6m
+ATSC7m
+ATSC8m
+ATSC1v
+ATSC2v
+ATSC3v
+ATSC4v
+ATSC5v
+ATSC6v
+ATSC7v
+ATSC8v
+ATSC1e
+ATSC2e
+ATSC3e
+ATSC4e
+ATSC5e
+ATSC6e
+ATSC7e
+ATSC8e
+ATSC1i
+ATSC2i
+ATSC3i
+ATSC4i
+ATSC5i
+ATSC6i
+ATSC7i
+ATSC8i
+ATSC1s
+ATSC3s
+AATSC1c
+AATSC4c
+AATSC5c
+AATSC6c
+AATSC7c
+AATSC8c
+AATSC2m
+AATSC3m
+AATSC4m
+AATSC5m
+AATSC6m
+AATSC7m
+AATSC8m
+AATSC0v
+AATSC4v
+AATSC5v
+AATSC6v
+AATSC7v
+AATSC8v
+AATSC3e
+AATSC4e
+AATSC5e
+AATSC6e
+AATSC7e
+AATSC8e
+AATSC1p
+AATSC2p
+AATSC3p
+AATSC4p
+AATSC3i
+AATSC4i
+AATSC5i
+AATSC6i
+AATSC7i
+AATSC8i
+AATSC1s
+AATSC2s
+AATSC3s
+AATSC4s
+AATSC6s
+AATSC8s
+MATS1c
+MATS3c
+MATS3m
+MATS4m
+MATS5m
+MATS6m
+MATS1e
+MATS3e
+MATS1s
+MATS2s
+MATS3s
+MATS4s
+MATS5s
+MATS7s
+GATS1c
+GATS2c
+GATS3c
+GATS4c
+GATS5c
+GATS6c
+GATS7c
+GATS8c
+GATS1m
+GATS2m
+GATS4m
+GATS5m
+GATS6m
+GATS7m
+GATS8m
+GATS2v
+GATS6v
+GATS7v
+GATS8v
+GATS1e
+GATS3e
+GATS4e
+GATS1p
+GATS3p
+GATS4p
+GATS5p
+GATS1i
+GATS2i
+GATS3i
+GATS4i
+GATS2s
+GATS3s
+GATS5s
+GATS6s
+GATS7s
+GATS8s
+SM1_DzZ
+VE1_DzZ
+VE3_DzZ
+VR1_DzZ
+VE1_Dzv
+VR2_Dzv
+VE2_Dze
+SpMAD_Dzp
+VE2_Dzp
+SpMAD_Dzs
+SM1_Dzs
+VE1_Dzs
+VR2_Dzs
+nBase
+BCUTw-1l
+BCUTw-1h
+BCUTc-1l
+BCUTc-1h
+BCUTp-1l
+BCUTp-1h
+nBondsD
+nBondsD2
+nBondsT
+SpMax3_Bhm
+SpMin1_Bhm
+SpMin2_Bhm
+SpMin4_Bhm
+SpMin7_Bhm
+SpMax1_Bhv
+SpMax2_Bhe
+SpMin8_Bhe
+SpMax1_Bhs
+SpMax3_Bhs
+C2SP1
+C1SP2
+C2SP2
+C3SP2
+C1SP3
+C2SP3
+C3SP3
+C4SP3
+SCH-3
+SCH-4
+SCH-5
+SCH-7
+SC-4
+SC-5
+VC-3
+SP-7
+ASP-0
+ASP-1
+ASP-4
+ASP-5
+ASP-6
+ASP-7
+AVP-0
+AVP-3
+AVP-4
+AVP-6
+AVP-7
+CrippenLogP
+VE1_Dt
+VE3_Dt
+VR1_Dt
+ECCEN
+nwHBd
+nHBint2
+nHBint3
+nHBint4
+nHBint5
+nHBint6
+nHBint7
+nHBint8
+nHBint9
+nHdNH
+nHsSH
+nHsNH2
+nHssNH
+nHaaNH
+nHtCH
+nHdCH2
+nHdsCH
+nHCsatu
+nHAvin
+nsCH3
+nssCH2
+nsssCH
+naaaC
+ndsN
+naaN
+nsssN
+naasN
+nssssNp
+ndO
+nssO
+naaO
+nsOm
+ndS
+naaS
+ndssS
+nddssS
+SwHBa
+SHBint10
+SHsOH
+SsssCH
+SdssC
+SaasC
+SssssC
+SssS
+SsBr
+SsI
+minHBd
+minHBa
+minwHBa
+minHBint2
+minHBint3
+minHBint4
+minHBint5
+minHBint6
+minHBint7
+minHBint8
+minHBint9
+minHBint10
+minHsOH
+minHsNH2
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b3clf/geometry_opt.py

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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 pandas as pd
+from rdkit import Chem
+from rdkit.Chem import AllChem
+
+"""Convert SMILES to 3D and/or minimize the geometry from SDF with force field."""
+
+
+def geometry_optimize(input_fname,
+                      output_sdf,
+                      steps_opt=10000,
+                      # convergence=1.e-7,
+                      tool="rdkit",
+                      # optimization="cg",
+                      force_field="MMFF94s",
+                      smi_col=None,
+                      sep="\s+|t+"):
+    """Generate 3D coordinates and run geometry optimization with force field."""
+
+    # optimize the 3d coordinates
+    # use RDKit to minimize the geometry
+    if tool.lower() == "rdkit":
+        minimize_with_rdkit(input_molfname=input_fname,
+                            sdf_out=output_sdf,
+                            maxIters=steps_opt,
+                            force_field=force_field,
+                            smi_col=smi_col,
+                            sep=sep)
+    # use openbabel to minimize the geometry
+    elif tool == "openbabel":
+        # minimize_with_openbabel(input_molfname=input_fname,
+        #                         sdf_out=output_sdf,
+        #                         steps=steps_opt,
+        #                         optimization=optimization,
+        #                         convergence=convergence,
+        #                         force_field=force_field,
+        #                         smi_col=smi_col)
+        raise ValueError("OpenBabel is not supported yet.")
+    else:
+        raise ValueError("{} not implemented yet.".format(tool))
+
+
+def minimize_with_rdkit(input_molfname,
+                        sdf_out,
+                        smi_col=None,
+                        mol_name_col=None,
+                        maxIters=400,
+                        force_field="MMFF94s",
+                        sep="\s+"):
+    """Add hydrogen for 3D coordinates and minimize the geometry with RdKit."""
+    # load molecules
+    if input_molfname.lower().endswith(".smi") or input_molfname.lower().endswith(".csv"):
+        # todo: support .txt files
+        # todo: add support of more flexible separators
+        # todo: fix problem when mol_name is empty
+        df_mol = pd.read_csv(input_molfname, sep=sep, engine="python", header=None)
+        if df_mol.shape[1] == 1:
+            # Case for only SMILES column
+            smile_list = df_mol.iloc[:, -1].to_list()
+            mol_name_list = df_mol.iloc[:, -1].to_list()
+        else:
+            # Case for SMILES and MOL name columns
+            if smi_col is None:
+                smile_list = df_mol.iloc[:, 0].to_list()
+            else:
+                smile_list = df_mol[smi_col].to_list()
+
+            if mol_name_col is None:
+            # todo: use name if column name is valid
+                mol_name_list = df_mol.iloc[:, -1].to_list()
+            else:
+                mol_name_list = df_mol[mol_name_col].to_list()
+
+        mols = []
+        for idx, smi in enumerate(smile_list):
+            mol = Chem.MolFromSmiles(smi)
+            # This will overwrite
+            if mol is not None:
+                mol.SetProp("_Name", mol_name_list[idx])
+                mols.append(mol)
+
+    elif input_molfname.lower().endswith(".sdf"):
+        suppl = Chem.SDMolSupplier(input_molfname,
+                                   sanitize=True,
+                                   removeHs=False,
+                                   strictParsing=True)
+        mols = [mol for mol in suppl]
+        for idx, mol in enumerate(mols):
+            if (mol.GetProp("_Name") == "") or (mol.GetProp("_Name") is None):
+                smi = Chem.MolToSmiles(mol)
+                mol.SetProp("_Name", smi)
+                mols[idx] = mol
+
+    writer = Chem.SDWriter(sdf_out)
+    for idx, mol in enumerate(mols):
+        mol = Chem.AddHs(mol)
+        if force_field == "MMFF94s":
+            # use MMFF~ force field if possible
+
+            # taken from
+            # https://open-babel.readthedocs.io/en/latest/Forcefields/mmff94.html
+            # Some experiments and most theoretical calculations show significant pyramidal
+            # “puckering” at nitrogens in isolated structures. The MMFF94s (static) variant has
+            # slightly different  out-of-plane bending and dihedral torsion parameters to planarize
+            # certain types of delocalized trigonal N atoms, such as aromatic aniline. This provides
+            # a better match to the time-average molecular geometry in solution or crystal
+            # structures.
+            #
+            # If you are comparing force-field optimized molecules to crystal structure geometries,
+            # we recommend using the MMFF94s variant for this reason. All other parameters are
+            # identical. However, if you are performing “docking” simulations, consideration of
+            # active solution conformations, or other types of computational studies, we recommend
+            # using the MMFF94 variant, since one form or another of the N geometry will
+            # predominate.
+
+            AllChem.EmbedMolecule(mol, randomSeed=999)
+            # the following code will raise some errors
+            mini_tag = AllChem.MMFFOptimizeMolecule(mol, force_field, maxIters=maxIters)
+            # 0 optimize converged
+            # -1 can not set up force field
+            # 1 more iterations required
+            if mini_tag == 0:
+                writer.write(mol)
+            else:
+                if mini_tag == 1:
+                    AllChem.MMFFOptimizeMolecule(mol, force_field, maxIters=maxIters * 2)
+                elif mini_tag == -1:
+                    AllChem.UFFOptimizeMolecule(mol, maxIters=400)
+                writer.write(mol)
+
+        elif force_field == "uff":
+            # use uff force field if possible
+            AllChem.EmbedMolecule(mol, randomSeed=999)
+            # the following code will raise some errors
+            mini_tag = AllChem.UFFOptimizeMolecule(mol, maxIters=maxIters)
+            # 0 optimize converged
+            # -1 can not set up force field
+            # 1 more iterations required
+            if mini_tag == 0:
+                writer.write(mol)
+            else:
+                if mini_tag == 1:
+                    AllChem.UFFOptimizeMolecule(mol, maxIters=maxIters * 2)
+                elif mini_tag == -1:
+                    AllChem.MMFFOptimizeMolecule(mol, "MMFF94s", maxIters=maxIters)
+                writer.write(mol)
+
+        else:
+            raise NotImplementedError("This method is not implemented yet.")
+
+    writer.close()
+
+# todo: now the implementation is not supporting adding molecule name (such as SMILES strings)
+# def minimize_with_openbabel(input_molfname,
+#                             sdf_out,
+#                             steps=10000,
+#                             convergence=1.e-7,
+#                             optimization="cg",
+#                             force_field="GAFF",
+#                             smi_col=None):
+#     """Minimize the geometries with openbabel.
+#
+#     Parameters
+#     ----------
+#     input_molfname : str
+#         Input molecule fie name.
+#     sdf_out : str
+#         Output molecule file name.
+#     steps : int, optional
+#         Specify the maximum number of steps. default=2500.
+#     optimization : str, optional
+#         Use conjugate gradients ("cg") or steepest descent ("sd") algorithm for optimization.
+#         Default="cg".
+#     convergence : float, optional
+#         convergence threshold. Default=1.e-7.
+#     force_field : str, optional
+#         ForceField name including Generalized Amber Force Field (gaff), Ghemical Force Field
+#         (ghemical), MMFF94 Force Field (mmff94) and Universal Force Field (uff). Default="gaff".
+#     """
+#
+#     # https://open-babel.readthedocs.io/en/latest/Command-line_tools/babel.html#forcefield-energy-and-minimization
+#     subprocess.Popen(["obabel", input_molfname, "-h", "-O", sdf_out,
+#                       "--gen3d", "--minimize",
+#                       "--n", str(steps), "--sd", optimization, "--crit",
+#                       str(convergence), "--ff", force_field])
+#     print("Geometry optimization with OpenBabel is done.")

b3clf/pre_trained/b3clf_dtree_borderline_SMOTE.joblib

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+H1_Bepotastine
+     RDKit          3D
+
+ 52 54  0  0  1  0  0  0  0  0999 V2000
+    6.2601    3.8627   -0.7580 Cl  0  0  0  0  0  0  0  0  0  0  0  0
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+M  END
+>  <compoud_name>  (1)
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+>  <SMILES>  (1)
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+>  <cid>  (1)
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+>  <Energy>  (1)
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+$$$$
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+>  <cid>  (2)
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+$$$$
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+ 18 24  2  0
+ 18 25  1  0
+ 19 22  2  0
+ 19 45  1  0
+ 20 23  2  0
+ 20 46  1  0
+ 21 26  1  0
+ 21 47  1  0
+ 22 23  1  0
+ 22 48  1  0
+ 24 28  1  0
+ 24 49  1  0
+ 25 50  1  0
+ 26 27  2  0
+ 26 51  1  0
+ 27 52  1  0
+ 28 29  2  0
+ 28 30  1  0
+ 29 53  1  0
+ 30 54  1  0
+ 30 55  1  0
+ 30 56  1  0
+M  END
+>  <compoud_name>  (3)
+H1_Rupatadine
+
+>  <SMILES>  (3)
+[H]c1nc2c(c([H])c1[H])C([H])([H])C([H])([H])c1c([H])c(Cl)c([H])c([H])c1C2=C1C([H])([H])C([H])([H])N(C([H])([H])c2c([H])nc([H])c(C([H])([H])[H])c2[H])C([H])([H])C1([H])[H]
+
+>  <cid>  (3)
+133017
+
+>  <category>  (3)
+N
+
+>  <inchi>  (3)
+InChI=1S/C26H26ClN3/c1-18-13-19(16-28-15-18)17-30-11-8-20(9-12-30)25-24-7-6-23(27)14-22(24)5-4-21-3-2-10-29-26(21)25/h2-3,6-7,10,13-16H,4-5,8-9,11-12,17H2,1H3
+
+>  <Energy>  (3)
+119.976
+
+$$$$

b3clf/utils.py

@@ -0,0 +1,161 @@
+# -*- 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/>
+#
+# --
+
+"""B3clf utility functions."""
+
+import os
+
+import numpy as np
+import pandas as pd
+from joblib import load
+
+__all__ = [
+    "get_descriptors",
+    "select_descriptors",
+    "scale_descriptors",
+    "get_clf",
+    "predict_permeability",
+]
+
+
+def get_descriptors(df):
+    """Create features dataframe and information dataframe from provided path."""
+    if type(df) == str:
+        if df.lower().endswith(".sdf"):
+            df = pd.read_sdf(df)
+        elif df.lower().endswith(".xlsx"):
+            df = pd.read_excel(df, engine="openpyxl")
+        elif df.lower().endswith(".csv"):
+            df = pd.read_csv(df)
+        else:
+            raise ValueError(
+                "Command-line tool only supports feature files in .XLSX format"
+            )
+
+    info_list = ["compoud_name", "SMILES", "cid", "category", "inchi", "Energy"]
+
+    # drop infinity and NaN values
+    df.replace([np.inf, -np.inf], np.nan, inplace=True)
+    df.dropna(axis=0, inplace=True)
+
+    features_cols = [col for col in df.columns.to_list() if col not in info_list]
+    X = df[features_cols]
+    info_cols = [col for col in df.columns.to_list() if col in info_list]
+    if len(info_cols) != 0:
+        info = df[info_cols]
+    else:
+        info = pd.DataFrame(index=df.index)
+
+    return X, info
+
+
+def select_descriptors(df):
+    """Select certain Padel descriptors, which are those taken by B3clf models."""
+    dirname = os.path.dirname(__file__)
+    with open(os.path.join(dirname, "feature_list.txt")) as f:
+        selected_list = f.read().splitlines()
+
+    df_selected = df[[col for col in df.columns.to_list() if col in selected_list]]
+
+    return df_selected
+
+
+def scale_descriptors(df):
+    """Scale input features using B3DB Standard Scaler.
+
+    The b3db_scaler was fitted using the full B3DB dataset.
+    """
+
+    dirname = os.path.dirname(__file__)
+    filename = os.path.join(dirname, "pre_trained", "b3clf_scaler.joblib")
+    b3db_scaler = load(filename)
+    df.iloc[:, :] = b3db_scaler.transform(df)
+
+    return df
+
+
+def get_clf(clf_str, sampling_str):
+    """Get b3clf fitted classifier"""
+    clf_list = ["dtree", "knn", "logreg", "xgb"]
+    sampling_list = [
+        "borderline_SMOTE",
+        "classic_ADASYN",
+        "classic_RandUndersampling",
+        "classic_SMOTE",
+        "kmeans_SMOTE",
+        "common",
+    ]
+
+    # This could be moved to an initial check method for input parameters
+    if clf_str not in clf_list:
+        raise ValueError("Input classifier is not supported; got {}".format(clf_str))
+    elif sampling_str not in sampling_list:
+        raise ValueError(
+            "Input sampling method is not supported; got {}".format(sampling_str)
+        )
+
+    dirname = os.path.dirname(__file__)
+    # Move data to new storage place for packaging
+    clf_path = os.path.join(
+        dirname, "pre_trained", "b3clf_{}_{}.joblib".format(clf_str, sampling_str)
+    )
+
+    clf = load(clf_path)
+
+    return clf
+
+
+def predict_permeability(clf_str, sampling_str, features_df, info_df, threshold="none"):
+    """Compute and store BBB predicted label and predicted probability to results dataframe."""
+
+    # load the threshold data
+    dirname = os.path.dirname(__file__)
+    fpath_thres = os.path.join(dirname, "data", "B3clf_thresholds.xlsx")
+    df_thres = pd.read_excel(fpath_thres, index_col=0, engine="openpyxl")
+    # default threshold is 0.5
+    label_pool = np.zeros(features_df.shape[0], dtype=int)
+
+    # get the classifier
+    clf = get_clf(clf_str=clf_str, sampling_str=sampling_str)
+
+    if features_df.index.tolist() != info_df.index.tolist():
+        raise ValueError(
+            "Features_df and Info_df do not have the same index. Internal processing error"
+        )
+
+    # get predicted probabilities
+    info_df.loc[:, "B3clf_predicted_probability"] = clf.predict_proba(features_df)[:, 1]
+    # get predicted label from probability using the threshold
+    mask = np.greater_equal(
+        info_df["B3clf_predicted_probability"].to_numpy(),
+        # df_thres.loc[clf_str + "-" + sampling_str, threshold])
+        df_thres.loc["xgb-classic_ADASYN", threshold],
+    )
+    label_pool[mask] = 1
+    # save the predicted labels
+    info_df["B3clf_predicted_label"] = label_pool
+
+    # info_df["B3clf_predicted_label"] = info_df["B3clf_predicted_label"].astype("int64")
+    info_df.reset_index(inplace=True)
+
+    return info_df

b3clf/version.py

@@ -0,0 +1,29 @@
+# -*- 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/>
+#
+# --
+
+"""Version Information for B3clf."""
+
+VERSION = (0, 0, 1, "beta")
+
+__version__ = ".".join(map(str, VERSION[:-1]))
+__release__ = ".".join(map(str, VERSION))

requirements.txt

@@ -0,0 +1,9 @@
+numpy>=1.21.4
+scipy>=1.7.2
+scikit-learn==0.24.2
+joblib>=1.1.0
+pandas>=1.3.4
+openpyxl>=3.0.9
+# rdkit-pypi>=2020.09.1.0
+xgboost==1.4.2
+padelpy>=0.1.11

requirements_conda.txt

@@ -0,0 +1,9 @@
+numpy>=1.21.4
+scipy>=1.7.2
+scikit-learn==0.24.2
+joblib>=1.1.0
+pandas>=1.3.4
+openpyxl>=3.0.9
+xgboost==1.4.2
+
+# pip install git+https://github.com/fwmeng88/padelpy.git@master

setup.py

@@ -0,0 +1,83 @@
+# -*- 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/>
+#
+# --
+
+"""Installation script for B3clf.
+
+Directly calling this script is only needed by B3clf developers in special
+circumstances. End users are recommended to install B3clf with pip.
+"""
+
+import os
+
+from setuptools import find_packages, setup
+
+
+def get_version_info():
+    """Read __version__ and DEV_CLASSIFIER from version.py, using exec, not import."""
+    fn_version = os.path.join("b3clf", "version.py")
+    if os.path.isfile(fn_version):
+        myglobals = {}
+        with open(fn_version, "r") as f:
+            exec(f.read(), myglobals)  # pylint: disable=exec-used
+        return myglobals["__version__"]
+    return "0.0.0.post0"
+
+
+def get_readme():
+    """Load README.md."""
+    with open("README.md") as fhandle:
+        return fhandle.read()
+
+
+VERSION = get_version_info()
+
+setup(
+    name="b3clf",
+    version=VERSION,
+    description="Models for blood-brain barrier classifications with resampling strategies.",
+    long_description=get_readme(),
+    author="Ayers Lab",
+    author_email="[email protected]",
+    url="https://github.com/theochem/B3clf",
+    package_dir={"B3clf": "b3clf"},
+    # packages=["b3clf"],
+    packages=find_packages(),
+    include_package_data=True,
+    entry_points={
+        "console_scripts": ["b3clf = b3clf.__main__:main"]
+    },
+    classifiers=[
+        "Environment :: Console",
+        "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)",
+        # todo: check if it works in mac and windows
+        "Operating System :: POSIX :: Linux",
+        "Programming Language :: Python :: 3",
+        "Topic :: Scientific/Engineering :: Chemistry",
+        "Topic :: Science/Engineering :: Molecular Science",
+        "Topic :: Scientific/Engineering :: Bio-Informatics",
+        "Intended Audience :: Science/Research",
+    ],
+    python_requires=">=3.7.0",
+    setup_requires=["numpy>=1.21.4", "scipy>=1.7.2"],
+    install_requires=["numpy>=1.21.4", "scipy>=1.7.2", "scikit-learn==0.24.2"],
+)