#
# 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
#
# A small modification is performed
#
# DGL team, August 2019
#
from __future__ import print_function

import math
import os

from rdkit import Chem
from rdkit.Chem import rdMolDescriptors
from rdkit.six.moves import cPickle
from rdkit.six import iteritems

from dgl.data.utils import download, _get_dgl_url

_fscores = None

def readFragmentScores(name='fpscores'):
    import gzip
    global _fscores
    fname = '{}.pkl.gz'.format(name)
    download(_get_dgl_url(os.path.join('dataset', fname)), path=fname)
    _fscores = cPickle.load(gzip.open(fname))
    outDict = {}
    for i in _fscores:
        for j in range(1, len(i)):
            outDict[i[j]] = float(i[0])
    _fscores = outDict

def numBridgeheadsAndSpiro(mol):
    nSpiro = rdMolDescriptors.CalcNumSpiroAtoms(mol)
    nBridgehead = rdMolDescriptors.CalcNumBridgeheadAtoms(mol)
    return nBridgehead, nSpiro

def calculateScore(m):
    if _fscores is None:
        readFragmentScores()

    # fragment score
    # 2 is the *radius* of the circular fingerprint
    fp = rdMolDescriptors.GetMorganFingerprint(m, 2)
    fps = fp.GetNonzeroElements()
    score1 = 0.
    nf = 0
    for bitId, v in iteritems(fps):
        nf += v
        sfp = bitId
        score1 += _fscores.get(sfp, -4) * v
    # We add L63 to avoid ZeroDivisionError.
    if nf != 0:
        score1 /= nf

    # features score
    nAtoms = m.GetNumAtoms()
    nChiralCenters = len(Chem.FindMolChiralCenters(m, includeUnassigned=True))
    ri = m.GetRingInfo()
    nBridgeheads, nSpiro = numBridgeheadsAndSpiro(m)
    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

def processMols(mols):
    print('smiles\tName\tsa_score')
    for i, m in enumerate(mols):
        if m is None:
            continue

    s = calculateScore(m)

    smiles = Chem.MolToSmiles(m)
    print(smiles + "\t" + m.GetProp('_Name') + "\t%3f" % s)

if __name__ == '__main__':
    import sys, time

    t1 = time.time()
    readFragmentScores("fpscores")
    t2 = time.time()

    suppl = Chem.SmilesMolSupplier(sys.argv[1])
    t3 = time.time()
    processMols(suppl)
    t4 = time.time()

    print('Reading took %.2f seconds. Calculating took %.2f seconds' % ((t2 - t1), (t4 - t3)),
          file=sys.stderr)

#
#  Copyright (c) 2013, Novartis Institutes for BioMedical Research Inc.
#  All rights reserved.
#
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# modification, are permitted provided that the following conditions are
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#       with the distribution.
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