Python Chem.FindMolChiralCenters方法代码示例

# 需要导入模块: from rdkit import Chem [as 别名]
# 或者: from rdkit.Chem import FindMolChiralCenters [as 别名]
def getMolSvg(self):
        self.drawer = rdMolDraw2D.MolDraw2DSVG(300,300)
        #TODO, what if self._drawmol doesn't exist?
        if self._drawmol != None:
            #Chiral tags on R/S
            chiraltags = Chem.FindMolChiralCenters(self._drawmol)
            opts = self.drawer.drawOptions()
            for tag in chiraltags:
                idx = tag[0]
                opts.atomLabels[idx]= self._drawmol.GetAtomWithIdx(idx).GetSymbol() + ':' + tag[1]
            if len(self._selectedAtoms) > 0:
                colors={self._selectedAtoms[-1]:(1,0.2,0.2)} #Color lastly selected a different color
                self.drawer.DrawMolecule(self._drawmol, highlightAtoms=self._selectedAtoms, highlightAtomColors=colors, )
            else:
                self.drawer.DrawMolecule(self._drawmol)
        self.drawer.FinishDrawing()
        self.finishedDrawing.emit()#Signal that drawer has finished
        svg = self.drawer.GetDrawingText().replace('svg:','')
        return svg 

# 需要导入模块: from rdkit import Chem [as 别名]
# 或者: from rdkit.Chem import FindMolChiralCenters [as 别名]
def _get_chiral_centers(self, mol):
        """
        Use RDKit to find the chiral centers with CIP(R/S) label

        This provides the absolute stereochemistry.  The chiral label obtained
        from pybabel and rdkit.mol.getchiraltag is relative positions of the bonds as provided

        Args:
            mol (Molecule): Molecule to asses
        Return:
            (dict): Keys are the atom index and values are the CIP label
        """
        mol_rdk = self._get_rdk_mol(mol, 'smiles')
        if mol_rdk is None:
            # Conversion to RDKit has failed
            return {}
        else:
            chiral_cc = Chem.FindMolChiralCenters(mol_rdk)
            return dict(chiral_cc) 

# 需要导入模块: from rdkit import Chem [as 别名]
# 或者: from rdkit.Chem import FindMolChiralCenters [as 别名]
def get_priority(self, retroPrecursor, **kwargs):
        if not self._loaded:
            self.load_model()

        necessary_reagent_atoms = retroPrecursor.necessary_reagent.count('[') / 2.
        scores = []
        for smiles in retroPrecursor.smiles_list:
            # If buyable, basically free
            ppg = self.pricer.lookup_smiles(smiles, alreadyCanonical=True)
            if ppg:
                scores.append(- ppg / 5.0)
                continue

            # Else, use heuristic
            x = Chem.MolFromSmiles(smiles)
            total_atoms = x.GetNumHeavyAtoms()
            ring_bonds = sum([b.IsInRing() - b.GetIsAromatic()
                              for b in x.GetBonds()])
            chiral_centers = len(Chem.FindMolChiralCenters(x))

            scores.append(
                - 2.00 * np.power(total_atoms, 1.5)
                - 1.00 * np.power(ring_bonds, 1.5)
                - 2.00 * np.power(chiral_centers, 2.0)
            )

        return np.sum(scores) - 4.00 * np.power(necessary_reagent_atoms, 2.0) 

# 需要导入模块: from rdkit import Chem [as 别名]
# 或者: from rdkit.Chem import FindMolChiralCenters [as 别名]
def get_priority(self, retroPrecursor, **kwargs):
        if not self._loaded:
            self.load_model()

        necessary_reagent_atoms = retroPrecursor.necessary_reagent.count('[') / 2.
        scores = []
        for smiles in retroPrecursor.smiles_list:
            # If buyable, basically free
            ppg = self.pricer.lookup_smiles(smiles, alreadyCanonical=True)
            if ppg:
                scores.append(- ppg / 1000.0)
                continue

            # Else, use heuristic
            x = Chem.MolFromSmiles(smiles)
            total_atoms = x.GetNumHeavyAtoms()
            ring_bonds = sum([b.IsInRing() - b.GetIsAromatic()
                              for b in x.GetBonds()])
            chiral_centers = len(Chem.FindMolChiralCenters(x))

            scores.append(
                - 2.00 * np.power(total_atoms, 1.5)
                - 1.00 * np.power(ring_bonds, 1.5)
                - 2.00 * np.power(chiral_centers, 2.0)
            )

        sco = np.sum(scores) - 4.00 * np.power(necessary_reagent_atoms, 2.0)
        return sco / retroPrecursor.template_score 

# 需要导入模块: from rdkit import Chem [as 别名]
# 或者: from rdkit.Chem import FindMolChiralCenters [as 别名]
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