#
# 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 __future__ import print_function

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

import math
from collections import defaultdict

import os.path as op

_fscores = None


def readFragmentScores(name='models/fpscores'):
  import gzip
  global _fscores
  # generate the full path filename:
  if name == "fpscores":
    name = op.join(op.dirname(__file__), name)
  _fscores = cPickle.load(gzip.open('%s.pkl.gz' % name))
  outDict = {}
  for i in _fscores:
    for j in range(1, len(i)):
      outDict[i[j]] = float(i[0])
  _fscores = outDict


def numBridgeheadsAndSpiro(mol, ri=None):
  nSpiro = rdMolDescriptors.CalcNumSpiroAtoms(mol)
  nBridgehead = rdMolDescriptors.CalcNumBridgeheadAtoms(mol)
  return nBridgehead, nSpiro


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 iteritems(fps):
    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


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)

#
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