Python rdkit.Chem.FindMolChiralCenters() Examples

The following are 9 code examples of rdkit.Chem.FindMolChiralCenters(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module rdkit.Chem , or try the search function .
Example #1
Source File: molViewWidget.py    From rdeditor with GNU Lesser General Public License v3.0 6 votes vote down vote up 
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
Example #2
Source File: molecule.py    From megnet with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
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)
Example #3
Source File: heuristic.py    From ASKCOS with Mozilla Public License 2.0 5 votes vote down vote up 
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)
Example #4
Source File: relevanceheuristic.py    From ASKCOS with Mozilla Public License 2.0 5 votes vote down vote up 
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
Example #5
Source File: sascorer.py    From icml18-jtnn with MIT License 4 votes vote down vote up 
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
Example #6
Source File: sascorer.py    From icml18-jtnn with MIT License 4 votes vote down vote up 
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
Example #7
Source File: sascorer.py    From sdvae with MIT License 4 votes vote down vote up 
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
Example #8
Source File: sascorer.py    From sdvae with MIT License 4 votes vote down vote up 
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
Example #9
Source File: sascorer.py    From iclr19-graph2graph with MIT License 4 votes vote down vote up 
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