Source code for drugex.training.scorers.sascorer

#
# calculation of synthetic accessibility score as described in:
#
# Estimation of Synthetic Accessibility Score of Drug-like Molecules based on Molecular Complexity and Fragment Contributions
# Peter Ertl and Ansgar Schuffenhauer
# Journal of Cheminformatics 1:8 (2009)
# http://www.jcheminf.com/content/1/1/8
#
# several small modifications to the original paper are included
# particularly slightly different formula for marocyclic penalty
# and taking into account also molecule symmetry (fingerprint density)
#
# for a set of 10k diverse molecules the agreement between the original method
# as implemented in PipelinePilot and this implementation is r2 = 0.97
#
# peter ertl & greg landrum, september 2013
#


from rdkit import Chem
from rdkit.Chem import rdMolDescriptors
import pickle

import math
from collections import defaultdict

import os.path as op

_fscores = None


[docs]def readFragmentScores(name='fpscores'): import gzip global _fscores # generate the full path filename: if name == "fpscores": name = op.join(op.dirname(__file__), name) data = pickle.load(gzip.open('%s.pkl.gz' % name)) outDict = {} for i in data: for j in range(1, len(i)): outDict[i[j]] = float(i[0]) _fscores = outDict
[docs]def numBridgeheadsAndSpiro(mol, ri=None): nSpiro = rdMolDescriptors.CalcNumSpiroAtoms(mol) nBridgehead = rdMolDescriptors.CalcNumBridgeheadAtoms(mol) return nBridgehead, nSpiro
[docs]def calculateScore(m): if _fscores is None: readFragmentScores() # fragment score fp = rdMolDescriptors.GetMorganFingerprint(m, 2) # <- 2 is the *radius* of the circular fingerprint fps = fp.GetNonzeroElements() score1 = 0. nf = 0 for bitId, v in fps.items(): nf += v sfp = bitId score1 += _fscores.get(sfp, -4) * v score1 /= nf # features score nAtoms = m.GetNumAtoms() nChiralCenters = len(Chem.FindMolChiralCenters(m, includeUnassigned=True)) ri = m.GetRingInfo() nBridgeheads, nSpiro = numBridgeheadsAndSpiro(m, ri) nMacrocycles = 0 for x in ri.AtomRings(): if len(x) > 8: nMacrocycles += 1 sizePenalty = nAtoms**1.005 - nAtoms stereoPenalty = math.log10(nChiralCenters + 1) spiroPenalty = math.log10(nSpiro + 1) bridgePenalty = math.log10(nBridgeheads + 1) macrocyclePenalty = 0. # --------------------------------------- # This differs from the paper, which defines: # macrocyclePenalty = math.log10(nMacrocycles+1) # This form generates better results when 2 or more macrocycles are present if nMacrocycles > 0: macrocyclePenalty = math.log10(2) score2 = 0. - sizePenalty - stereoPenalty - spiroPenalty - bridgePenalty - macrocyclePenalty # correction for the fingerprint density # not in the original publication, added in version 1.1 # to make highly symmetrical molecules easier to synthetise score3 = 0. if nAtoms > len(fps): score3 = math.log(float(nAtoms) / len(fps)) * .5 sascore = score1 + score2 + score3 # need to transform "raw" value into scale between 1 and 10 min = -4.0 max = 2.5 sascore = 11. - (sascore - min + 1) / (max - min) * 9. # smooth the 10-end if sascore > 8.: sascore = 8. + math.log(sascore + 1. - 9.) if sascore > 10.: sascore = 10.0 elif sascore < 1.: sascore = 1.0 return sascore