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#
# calculation of natural product-likeness as described in:
#
# Natural Product-likeness Score and Its Application for Prioritization of Compound Libraries
# Peter Ertl, Silvio Roggo, and Ansgar Schuffenhauer
# Journal of Chemical Information and Modeling, 48, 68-74 (2008)
# http://pubs.acs.org/doi/abs/10.1021/ci700286x
#
# for the training of this model only openly available data have been used
# ~50,000 natural products collected from various open databases
# ~1 million drug-like molecules from ZINC as a "non-NP background"
#
# peter ertl, august 2015
#
from __future__ import print_function
from rdkit import Chem
from rdkit.Chem import rdMolDescriptors
import sys,math,gzip,pickle
import os.path
def readNPModel(filename=os.path.join(os.path.dirname(__file__), 'publicnp.model.gz')):
sys.stderr.write("reading NP model ...\n")
fscore = pickle.load(gzip.open(filename))
sys.stderr.write("model in\n")
return fscore
def scoreMol(mol,fscore):
if mol is None:
raise ValueError('invalid molecule')
fp = rdMolDescriptors.GetMorganFingerprint(mol,2)
bits = fp.GetNonzeroElements()
# calculating the score
score = 0.
for bit in bits:
score += fscore.get(bit,0)
score /= float(mol.GetNumAtoms())
# preventing score explosion for exotic molecules
if score > 4:
score = 4. + math.log10(score - 4. + 1.)
if score < -4:
score = -4. - math.log10(-4. -score + 1.)
return score
def processMols(fscore,suppl):
sys.stderr.write("calculating ...\n")
count = {}
n = 0
for i,m in enumerate(suppl):
if m is None:
continue
n += 1
score = "%.3f" % scoreMol(m,fscore)
smiles = Chem.MolToSmiles(m,True)
name = m.GetProp('_Name')
print(smiles + "\t" + name + "\t" + score)
sys.stderr.write("finished, " + str(n) + " molecules processed\n")
if __name__=='__main__':
fscore=readNPModel() # fills fscore
suppl = Chem.SmilesMolSupplier(sys.argv[1],smilesColumn=0,nameColumn=1,titleLine=False)
processMols(fscore,suppl)
#
# Copyright (c) 2015, 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.
#
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