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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
#
import gzip
import math
import os.path
import pickle
import sys
from collections import namedtuple
from rdkit import Chem
from rdkit.Chem import rdFingerprintGenerator
def readNPModel(filename=os.path.join(os.path.dirname(__file__), 'publicnp.model.gz')):
"""Reads and returns the scoring model,
which has to be passed to the scoring functions."""
print("reading NP model ...", file=sys.stderr)
fscore = pickle.load(gzip.open(filename))
print("model in", file=sys.stderr)
return fscore
def scoreMolWConfidence(mol, fscore):
"""Next to the NP Likeness Score, this function outputs a confidence value
between 0..1 that descibes how many fragments of the tested molecule
were found in the model data set (1: all fragments were found).
Returns namedtuple NPLikeness(nplikeness, confidence)"""
if mol is None:
raise ValueError('invalid molecule')
fp = rdFingerprintGenerator.GetMorganGenerator(
radius=2, fpSize=2048
).GetSparseCountFingerprint(mol)
bits = fp.GetNonzeroElements()
# calculating the score
score = 0.0
bits_found = 0
for bit in bits:
if bit in fscore:
bits_found += 1
score += fscore[bit]
score /= float(mol.GetNumAtoms())
confidence = float(bits_found / len(bits))
# preventing score explosion for exotic molecules
if score > 4:
score = 4. + math.log10(score - 4. + 1.)
elif score < -4:
score = -4. - math.log10(-4. - score + 1.)
NPLikeness = namedtuple("NPLikeness", "nplikeness,confidence")
return NPLikeness(score, confidence)
def scoreMol(mol, fscore):
"""Calculates the Natural Product Likeness of a molecule.
Returns the score as float in the range -5..5."""
return scoreMolWConfidence(mol, fscore).nplikeness
def processMols(fscore, suppl):
print("calculating ...", file=sys.stderr)
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)
print("finished, " + str(n) + " molecules processed", file=sys.stderr)
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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