def GetTopologicalTorsionFingerprintAsIds(mol, targetSize=4):
iv = GetTopologicalTorsionFingerprint(mol, targetSize)
res = []
for k, v in iv.GetNonzeroElements().iteritems():
res.extend([k] * v)
res.sort()
return res
def GetTopologicalTorsionFingerprintAsIds(mol, targetSize=4):
nonZeroElements = GetTopologicalTorsionFingerprint(
mol, targetSize).GetNonzeroElements()
frequencies = sorted(nonZeroElements.items())
res = []
for k, v in frequencies:
res.extend([k] * v)
return res
def _transform_mol(self, mol):
""" Private method to transform a skchem molecule.
Args:
mol (skchem.Mol): Molecule to calculate fingerprint for.
Returns:
np.array or dict:
Fingerprint as an array (or a dict if sparse).
"""
if self.as_bits and self.n_feats > 0:
fp = GetHashedTopologicalTorsionFingerprintAsBitVect(
mol,
nBits=self.n_feats,
targetSize=self.target_size,
includeChirality=self.use_chirality)
res = np.array(0)
ConvertToNumpyArray(fp, res)
res = res.astype(np.uint8)
else:
if self.n_feats <= 0:
res = GetTopologicalTorsionFingerprint(
mol,
nBits=self.n_feats,
targetSize=self.target_size,
includeChirality=self.use_chirality)
res = res.GetNonzeroElements()
if self.as_bits:
res = {k: int(v > 0) for k, v in res.items()}
else:
res = GetHashedTopologicalTorsionFingerprint(
mol,
nBits=self.n_feats,
targetSize=self.target_size,
includeChirality=self.use_chirality)
res = np.array(list(res))
return res
def torsions(mol, **kwargs):
return list(
GetTopologicalTorsionFingerprint(mol, **kwargs).GetNonzeroElements())