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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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
