File: QED.py

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#
#  Copyright (c) 2009-2017, Novartis Institutes for BioMedical Research Inc.
#  All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
#     * Redistributions of source code must retain the above copyright
#       notice, this list of conditions and the following disclaimer.
#     * Redistributions in binary form must reproduce the above
#       copyright notice, this list of conditions and the following
#       disclaimer in the documentation and/or other materials provided
#       with the distribution.
#     * Neither the name of Novartis Institutes for BioMedical Research Inc.
#       nor the names of its contributors may be used to endorse or promote
#       products derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
"""

QED stands for quantitative estimation of drug-likeness and the concept was for the first time
introduced by Richard Bickerton and coworkers [1]. The empirical rationale of the QED measure
reflects the underlying distribution of molecular properties including molecular weight, logP,
topological polar surface area, number of hydrogen bond donors and acceptors, the number of
aromatic rings and rotatable bonds, and the presence of unwanted chemical functionalities.

The QED results as generated by the RDKit-based implementation of Biscu-it(tm) are not completely
identical to those from the original publication [1]. These differences are a consequence of
differences within the underlying calculated property calculators used in both methods. For
example, discrepancies can be noted in the results from the logP calculations, nevertheless
despite the fact that both approaches (Pipeline Pilot in the original publication and RDKit
in our Biscu-it(tm) implementation) mention to use the Wildman and Crippen methodology for the
calculation of their logP-values [2]. However, the differences in the resulting QED-values
are very small and are not compromising the usefulness of using Qed in your daily research.

[1] Bickerton, G.R.; Paolini, G.V.; Besnard, J.; Muresan, S.; Hopkins, A.L. (2012)
    'Quantifying the chemical beauty of drugs',
    Nature Chemistry, 4, 90-98 [https://doi.org/10.1038/nchem.1243]

[2] Wildman, S.A.; Crippen, G.M. (1999)
    'Prediction of Physicochemical Parameters by Atomic Contributions',
    Journal of Chemical Information and Computer Sciences, 39, 868-873 [https://doi.org/10.1021/ci990307l]

"""
from collections import namedtuple
import math

from rdkit import Chem
from rdkit.Chem import MolSurf, Crippen
from rdkit.Chem import rdMolDescriptors as rdmd
from rdkit.Chem.ChemUtils.DescriptorUtilities import setDescriptorVersion


QEDproperties = namedtuple('QEDproperties', 'MW,ALOGP,HBA,HBD,PSA,ROTB,AROM,ALERTS')
ADSparameter = namedtuple('ADSparameter', 'A,B,C,D,E,F,DMAX')

WEIGHT_MAX = QEDproperties(0.50, 0.25, 0.00, 0.50, 0.00, 0.50, 0.25, 1.00)
WEIGHT_MEAN = QEDproperties(0.66, 0.46, 0.05, 0.61, 0.06, 0.65, 0.48, 0.95)
WEIGHT_NONE = QEDproperties(1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00)

AliphaticRings = Chem.MolFromSmarts('[$([A;R][!a])]')

#
AcceptorSmarts = [
  '[oH0;X2]',
  '[OH1;X2;v2]',
  '[OH0;X2;v2]',
  '[OH0;X1;v2]',
  '[O-;X1]',
  '[SH0;X2;v2]',
  '[SH0;X1;v2]',
  '[S-;X1]',
  '[nH0;X2]',
  '[NH0;X1;v3]',
  '[$([N;+0;X3;v3]);!$(N[C,S]=O)]'
]
Acceptors = [Chem.MolFromSmarts(hba) for hba in AcceptorSmarts]

#
StructuralAlertSmarts = [
  '*1[O,S,N]*1',
  '[S,C](=[O,S])[F,Br,Cl,I]',
  '[CX4][Cl,Br,I]',
  '[#6]S(=O)(=O)O[#6]',
  '[$([CH]),$(CC)]#CC(=O)[#6]',
  '[$([CH]),$(CC)]#CC(=O)O[#6]',
  'n[OH]',
  '[$([CH]),$(CC)]#CS(=O)(=O)[#6]',
  'C=C(C=O)C=O',
  'n1c([F,Cl,Br,I])cccc1',
  '[CH1](=O)',
  '[#8][#8]',
  '[C;!R]=[N;!R]',
  '[N!R]=[N!R]',
  '[#6](=O)[#6](=O)',
  '[#16][#16]',
  '[#7][NH2]',
  'C(=O)N[NH2]',
  '[#6]=S',
  '[$([CH2]),$([CH][CX4]),$(C([CX4])[CX4])]=[$([CH2]),$([CH][CX4]),$(C([CX4])[CX4])]',
  'C1(=[O,N])C=CC(=[O,N])C=C1',
  'C1(=[O,N])C(=[O,N])C=CC=C1',
  'a21aa3a(aa1aaaa2)aaaa3',
  'a31a(a2a(aa1)aaaa2)aaaa3',
  'a1aa2a3a(a1)A=AA=A3=AA=A2',
  'c1cc([NH2])ccc1',
  '[Hg,Fe,As,Sb,Zn,Se,se,Te,B,Si,Na,Ca,Ge,Ag,Mg,K,Ba,Sr,Be,Ti,Mo,Mn,Ru,Pd,Ni,Cu,Au,Cd,' +
  'Al,Ga,Sn,Rh,Tl,Bi,Nb,Li,Pb,Hf,Ho]',
  'I',
  'OS(=O)(=O)[O-]',
  '[N+](=O)[O-]',
  'C(=O)N[OH]',
  'C1NC(=O)NC(=O)1',
  '[SH]',
  '[S-]',
  'c1ccc([Cl,Br,I,F])c([Cl,Br,I,F])c1[Cl,Br,I,F]',
  'c1cc([Cl,Br,I,F])cc([Cl,Br,I,F])c1[Cl,Br,I,F]',
  '[CR1]1[CR1][CR1][CR1][CR1][CR1][CR1]1',
  '[CR1]1[CR1][CR1]cc[CR1][CR1]1',
  '[CR2]1[CR2][CR2][CR2][CR2][CR2][CR2][CR2]1',
  '[CR2]1[CR2][CR2]cc[CR2][CR2][CR2]1',
  '[CH2R2]1N[CH2R2][CH2R2][CH2R2][CH2R2][CH2R2]1',
  '[CH2R2]1N[CH2R2][CH2R2][CH2R2][CH2R2][CH2R2][CH2R2]1',
  'C#C',
  '[OR2,NR2]@[CR2]@[CR2]@[OR2,NR2]@[CR2]@[CR2]@[OR2,NR2]',
  '[$([N+R]),$([n+R]),$([N+]=C)][O-]',
  '[#6]=N[OH]',
  '[#6]=NOC=O',
  '[#6](=O)[CX4,CR0X3,O][#6](=O)',
  'c1ccc2c(c1)ccc(=O)o2',
  '[O+,o+,S+,s+]',
  'N=C=O',
  '[NX3,NX4][F,Cl,Br,I]',
  'c1ccccc1OC(=O)[#6]',
  '[CR0]=[CR0][CR0]=[CR0]',
  '[C+,c+,C-,c-]',
  'N=[N+]=[N-]',
  'C12C(NC(N1)=O)CSC2',
  'c1c([OH])c([OH,NH2,NH])ccc1',
  'P',
  '[N,O,S]C#N',
  'C=C=O',
  '[Si][F,Cl,Br,I]',
  '[SX2]O',
  '[SiR0,CR0](c1ccccc1)(c2ccccc2)(c3ccccc3)',
  'O1CCCCC1OC2CCC3CCCCC3C2',
  'N=[CR0][N,n,O,S]',
  '[cR2]1[cR2][cR2]([Nv3X3,Nv4X4])[cR2][cR2][cR2]1[cR2]2[cR2][cR2][cR2]([Nv3X3,Nv4X4])[cR2][cR2]2',
  'C=[C!r]C#N',
  '[cR2]1[cR2]c([N+0X3R0,nX3R0])c([N+0X3R0,nX3R0])[cR2][cR2]1',
  '[cR2]1[cR2]c([N+0X3R0,nX3R0])[cR2]c([N+0X3R0,nX3R0])[cR2]1',
  '[cR2]1[cR2]c([N+0X3R0,nX3R0])[cR2][cR2]c1([N+0X3R0,nX3R0])',
  '[OH]c1ccc([OH,NH2,NH])cc1',
  'c1ccccc1OC(=O)O',
  '[SX2H0][N]',
  'c12ccccc1(SC(S)=N2)',
  'c12ccccc1(SC(=S)N2)',
  'c1nnnn1C=O',
  's1c(S)nnc1NC=O',
  'S1C=CSC1=S',
  'C(=O)Onnn',
  'OS(=O)(=O)C(F)(F)F',
  'N#CC[OH]',
  'N#CC(=O)',
  'S(=O)(=O)C#N',
  'N[CH2]C#N',
  'C1(=O)NCC1',
  'S(=O)(=O)[O-,OH]',
  'NC[F,Cl,Br,I]',
  'C=[C!r]O',
  '[NX2+0]=[O+0]',
  '[OR0,NR0][OR0,NR0]',
  'C(=O)O[C,H1].C(=O)O[C,H1].C(=O)O[C,H1]',
  '[CX2R0][NX3R0]',
  'c1ccccc1[C;!R]=[C;!R]c2ccccc2',
  '[NX3R0,NX4R0,OR0,SX2R0][CX4][NX3R0,NX4R0,OR0,SX2R0]',
  '[s,S,c,C,n,N,o,O]~[n+,N+](~[s,S,c,C,n,N,o,O])(~[s,S,c,C,n,N,o,O])~[s,S,c,C,n,N,o,O]',
  '[s,S,c,C,n,N,o,O]~[nX3+,NX3+](~[s,S,c,C,n,N])~[s,S,c,C,n,N]',
  '[*]=[N+]=[*]',
  '[SX3](=O)[O-,OH]',
  'N#N',
  'F.F.F.F',
  '[R0;D2][R0;D2][R0;D2][R0;D2]',
  '[cR,CR]~C(=O)NC(=O)~[cR,CR]',
  'C=!@CC=[O,S]',
  '[#6,#8,#16][#6](=O)O[#6]',
  'c[C;R0](=[O,S])[#6]',
  'c[SX2][C;!R]',
  'C=C=C',
  'c1nc([F,Cl,Br,I,S])ncc1',
  'c1ncnc([F,Cl,Br,I,S])c1',
  'c1nc(c2c(n1)nc(n2)[F,Cl,Br,I])',
  '[#6]S(=O)(=O)c1ccc(cc1)F',
  '[15N]',
  '[13C]',
  '[18O]',
  '[34S]'
]

StructuralAlerts = [Chem.MolFromSmarts(smarts) for smarts in StructuralAlertSmarts]

adsParameters = {
  'MW': ADSparameter(A=2.817065973, B=392.5754953, C=290.7489764, D=2.419764353, E=49.22325677,
                     F=65.37051707, DMAX=104.9805561),
  'ALOGP': ADSparameter(A=3.172690585, B=137.8624751, C=2.534937431, D=4.581497897, E=0.822739154,
                        F=0.576295591, DMAX=131.3186604),
  'HBA': ADSparameter(A=2.948620388, B=160.4605972, C=3.615294657, D=4.435986202, E=0.290141953,
                      F=1.300669958, DMAX=148.7763046),
  'HBD': ADSparameter(A=1.618662227, B=1010.051101, C=0.985094388, D=0.000000001, E=0.713820843,
                      F=0.920922555, DMAX=258.1632616),
  'PSA': ADSparameter(A=1.876861559, B=125.2232657, C=62.90773554, D=87.83366614, E=12.01999824,
                      F=28.51324732, DMAX=104.5686167),
  'ROTB': ADSparameter(A=0.010000000, B=272.4121427, C=2.558379970, D=1.565547684, E=1.271567166,
                       F=2.758063707, DMAX=105.4420403),
  'AROM': ADSparameter(A=3.217788970, B=957.7374108, C=2.274627939, D=0.000000001, E=1.317690384,
                       F=0.375760881, DMAX=312.3372610),
  'ALERTS': ADSparameter(A=0.010000000, B=1199.094025, C=-0.09002883, D=0.000000001, E=0.185904477,
                         F=0.875193782, DMAX=417.7253140),
}


def ads(x, adsParameter):
  """ ADS function """
  p = adsParameter
  exp1 = 1 + math.exp(-1 * (x - p.C + p.D / 2) / p.E)
  exp2 = 1 + math.exp(-1 * (x - p.C - p.D / 2) / p.F)
  dx = p.A + p.B / exp1 * (1 - 1 / exp2)
  return dx / p.DMAX


def properties(mol):
  """
  Calculates the properties that are required to calculate the QED descriptor.
  """
  if mol is None:
    raise ValueError('You need to provide a mol argument.')
  mol = Chem.RemoveHs(mol)
  qedProperties = QEDproperties(
    MW=rdmd._CalcMolWt(mol),
    ALOGP=Crippen.MolLogP(mol),
    HBA=sum(len(mol.GetSubstructMatches(pattern)) for pattern in Acceptors
            if mol.HasSubstructMatch(pattern)),
    HBD=rdmd.CalcNumHBD(mol),
    PSA=MolSurf.TPSA(mol),
    ROTB=rdmd.CalcNumRotatableBonds(mol, rdmd.NumRotatableBondsOptions.Strict),
    AROM=Chem.GetSSSR(Chem.DeleteSubstructs(Chem.Mol(mol), AliphaticRings)),
    ALERTS=sum(1 for alert in StructuralAlerts if mol.HasSubstructMatch(alert)),
  )
  # The replacement
  # AROM=Lipinski.NumAromaticRings(mol),
  # is not identical. The expression above tends to count more rings
  # N1C2=CC=CC=C2SC3=C1C=CC4=C3C=CC=C4
  # OC1=C(O)C=C2C(=C1)OC3=CC(=O)C(=CC3=C2C4=CC=CC=C4)O
  # CC(C)C1=CC2=C(C)C=CC2=C(C)C=C1  uses 2, should be 0 ?
  return qedProperties


@setDescriptorVersion(version='1.1.0')
def qed(mol, w=WEIGHT_MEAN, qedProperties=None):
  """ Calculate the weighted sum of ADS mapped properties

  some examples from the QED paper, reference values from Peter G's original implementation
  >>> m = Chem.MolFromSmiles('N=C(CCSCc1csc(N=C(N)N)n1)NS(N)(=O)=O')
  >>> qed(m)
  0.253...
  >>> m = Chem.MolFromSmiles('CNC(=NCCSCc1nc[nH]c1C)NC#N')
  >>> qed(m)
  0.234...
  >>> m = Chem.MolFromSmiles('CCCCCNC(=N)NN=Cc1c[nH]c2ccc(CO)cc12')
  >>> qed(m)
  0.234...
  """
  if qedProperties is None:
      qedProperties = properties(mol)
  d = [ads(pi, adsParameters[name]) for name, pi in qedProperties._asdict().items()]
  t = sum(wi * math.log(di) for wi, di in zip(w, d))
  return math.exp(t / sum(w))


def weights_max(mol):
  """
  Calculates the QED descriptor using maximal descriptor weights.
  """
  return qed(mol, w=WEIGHT_MAX)


def weights_mean(mol):
  """
  Calculates the QED descriptor using average descriptor weights.
  """
  return qed(mol, w=WEIGHT_MEAN)


def weights_none(mol):
  """
  Calculates the QED descriptor using unit weights.
  """
  return qed(mol, w=WEIGHT_NONE)


def default(mol):
  """
  Calculates the QED descriptor using average descriptor weights.
  """
  return weights_mean(mol)


# ------------------------------------
#
#  doctest boilerplate
#
def _runDoctests(verbose=None):  # pragma: nocover
  import sys
  import doctest
  failed, _ = doctest.testmod(optionflags=doctest.ELLIPSIS, verbose=verbose)
  sys.exit(failed)


if __name__ == '__main__':  # pragma: nocover
  _runDoctests()