rdkit.Chem.Pharm3D.EmbedLib module¶

rdkit.Chem.Pharm3D.EmbedLib.AddExcludedVolumes(bm, excludedVolumes, smoothIt=True)¶

Adds a set of excluded volumes to the bounds matrix and returns the new matrix

excludedVolumes is a list of ExcludedVolume objects

>>> boundsMat = numpy.array([[0.0, 2.0, 2.0],[1.0, 0.0, 2.0],[1.0, 1.0, 0.0]])
>>> ev1 = ExcludedVolume.ExcludedVolume(([(0, ), 0.5, 1.0], ), exclusionDist=1.5)
>>> bm = AddExcludedVolumes(boundsMat, (ev1, ))

the results matrix is one bigger:

>>> bm.shape == (4, 4)
True

and the original bounds mat is not altered:

>>> boundsMat.shape == (3, 3)
True

>>> print(', '.join([f'{x:.3f}' for x in bm[-1]]))
0.500, 1.500, 1.500, 0.000
>>> print(', '.join([f'{x:.3f}' for x in bm[:,-1]]))
1.000, 3.000, 3.000, 0.000

rdkit.Chem.Pharm3D.EmbedLib.Check2DBounds(atomMatch, mol, pcophore)¶

checks to see if a particular mapping of features onto a molecule satisfies a pharmacophore’s 2D restrictions

>>> from rdkit import Geometry
>>> from rdkit.Chem.Pharm3D import Pharmacophore
>>> activeFeats = [
...  ChemicalFeatures.FreeChemicalFeature('Acceptor', Geometry.Point3D(0.0, 0.0, 0.0)),
...  ChemicalFeatures.FreeChemicalFeature('Donor', Geometry.Point3D(0.0, 0.0, 0.0))]
>>> pcophore= Pharmacophore.Pharmacophore(activeFeats)
>>> pcophore.setUpperBound2D(0, 1, 3)
>>> m = Chem.MolFromSmiles('FCC(N)CN')
>>> Check2DBounds(((0, ), (3, )), m, pcophore)
True
>>> Check2DBounds(((0, ), (5, )), m, pcophore)
False

rdkit.Chem.Pharm3D.EmbedLib.CoarseScreenPharmacophore(atomMatch, bounds, pcophore, verbose=False)¶

>>> from rdkit import Geometry
>>> from rdkit.Chem.Pharm3D import Pharmacophore
>>> feats = [
...   ChemicalFeatures.FreeChemicalFeature('HBondAcceptor', 'HAcceptor1',
...                                        Geometry.Point3D(0.0, 0.0, 0.0)),
...   ChemicalFeatures.FreeChemicalFeature('HBondDonor', 'HDonor1',
...                                        Geometry.Point3D(2.65, 0.0, 0.0)),
...   ChemicalFeatures.FreeChemicalFeature('Aromatic', 'Aromatic1',
...                                        Geometry.Point3D(5.12, 0.908, 0.0)),
...   ]
>>> pcophore = Pharmacophore.Pharmacophore(feats)
>>> pcophore.setLowerBound(0, 1, 1.1)
>>> pcophore.setUpperBound(0, 1, 1.9)
>>> pcophore.setLowerBound(0, 2, 2.1)
>>> pcophore.setUpperBound(0, 2, 2.9)
>>> pcophore.setLowerBound(1, 2, 2.1)
>>> pcophore.setUpperBound(1, 2, 3.9)

>>> bounds = numpy.array([[0, 2, 3],[1, 0, 4],[2, 3, 0]], dtype=numpy.float64)
>>> CoarseScreenPharmacophore(((0, ),(1, )),bounds, pcophore)
True

>>> CoarseScreenPharmacophore(((0, ),(2, )),bounds, pcophore)
False

>>> CoarseScreenPharmacophore(((1, ),(2, )),bounds, pcophore)
False

>>> CoarseScreenPharmacophore(((0, ),(1, ),(2, )),bounds, pcophore)
True

>>> CoarseScreenPharmacophore(((1, ),(0, ),(2, )),bounds, pcophore)
False

>>> CoarseScreenPharmacophore(((2, ),(1, ),(0, )),bounds, pcophore)
False

# we ignore the point locations here and just use their definitions:

>>> feats = [
...   ChemicalFeatures.FreeChemicalFeature('HBondAcceptor', 'HAcceptor1',
...                                        Geometry.Point3D(0.0, 0.0, 0.0)),
...   ChemicalFeatures.FreeChemicalFeature('HBondDonor', 'HDonor1',
...                                        Geometry.Point3D(2.65, 0.0, 0.0)),
...   ChemicalFeatures.FreeChemicalFeature('Aromatic', 'Aromatic1',
...                                        Geometry.Point3D(5.12, 0.908, 0.0)),
...   ChemicalFeatures.FreeChemicalFeature('HBondDonor', 'HDonor1',
...                                        Geometry.Point3D(2.65, 0.0, 0.0)),
...                ]
>>> pcophore=Pharmacophore.Pharmacophore(feats)
>>> pcophore.setLowerBound(0,1, 2.1)
>>> pcophore.setUpperBound(0,1, 2.9)
>>> pcophore.setLowerBound(0,2, 2.1)
>>> pcophore.setUpperBound(0,2, 2.9)
>>> pcophore.setLowerBound(0,3, 2.1)
>>> pcophore.setUpperBound(0,3, 2.9)
>>> pcophore.setLowerBound(1,2, 1.1)
>>> pcophore.setUpperBound(1,2, 1.9)
>>> pcophore.setLowerBound(1,3, 1.1)
>>> pcophore.setUpperBound(1,3, 1.9)
>>> pcophore.setLowerBound(2,3, 1.1)
>>> pcophore.setUpperBound(2,3, 1.9)
>>> bounds = numpy.array([[0, 3, 3, 3],
...                       [2, 0, 2, 2],
...                       [2, 1, 0, 2],
...                       [2, 1, 1, 0]],
...                      dtype=numpy.float64)

>>> CoarseScreenPharmacophore(((0, ), (1, ), (2, ), (3, )), bounds, pcophore)
True

>>> CoarseScreenPharmacophore(((0, ), (1, ), (3, ), (2, )), bounds, pcophore)
True

>>> CoarseScreenPharmacophore(((1, ), (0, ), (3, ), (2, )), bounds, pcophore)
False

rdkit.Chem.Pharm3D.EmbedLib.CombiEnum(sequence)¶

This generator takes a sequence of sequences as an argument and provides all combinations of the elements of the subsequences:

>>> gen = CombiEnum(((1, 2), (10, 20)))
>>> next(gen)
[1, 10]
>>> next(gen)
[1, 20]

>>> [x for x in CombiEnum(((1, 2), (10,20)))]
[[1, 10], [1, 20], [2, 10], [2, 20]]

>>> [x for x in CombiEnum(((1, 2),(10, 20), (100, 200)))]
[[1, 10, 100], [1, 10, 200], [1, 20, 100], [1, 20, 200], [2, 10, 100],
 [2, 10, 200], [2, 20, 100], [2, 20, 200]]

rdkit.Chem.Pharm3D.EmbedLib.ComputeChiralVolume(mol, centerIdx, confId=-1)¶

Computes the chiral volume of an atom

We’re using the chiral volume formula from Figure 7 of Blaney and Dixon, Rev. Comp. Chem. V, 299-335 (1994)

>>> import os.path
>>> from rdkit import RDConfig
>>> dataDir = os.path.join(RDConfig.RDCodeDir,'Chem/Pharm3D/test_data')

R configuration atoms give negative volumes:

>>> mol = Chem.MolFromMolFile(os.path.join(dataDir, 'mol-r.mol'))
>>> Chem.AssignStereochemistry(mol)
>>> mol.GetAtomWithIdx(1).GetProp('_CIPCode')
'R'
>>> ComputeChiralVolume(mol, 1) < 0
True

S configuration atoms give positive volumes:

>>> mol = Chem.MolFromMolFile(os.path.join(dataDir, 'mol-s.mol'))
>>> Chem.AssignStereochemistry(mol)
>>> mol.GetAtomWithIdx(1).GetProp('_CIPCode')
'S'
>>> ComputeChiralVolume(mol, 1) > 0
True

Non-chiral (or non-specified) atoms give zero volume:

>>> ComputeChiralVolume(mol, 0) == 0.0
True

We also work on 3-coordinate atoms (with implicit Hs):

>>> mol = Chem.MolFromMolFile(os.path.join(dataDir, 'mol-r-3.mol'))
>>> Chem.AssignStereochemistry(mol)
>>> mol.GetAtomWithIdx(1).GetProp('_CIPCode')
'R'
>>> ComputeChiralVolume(mol, 1) < 0
True

>>> mol = Chem.MolFromMolFile(os.path.join(dataDir, 'mol-s-3.mol'))
>>> Chem.AssignStereochemistry(mol)
>>> mol.GetAtomWithIdx(1).GetProp('_CIPCode')
'S'
>>> ComputeChiralVolume(mol, 1) > 0
True

rdkit.Chem.Pharm3D.EmbedLib.ConstrainedEnum(matches, mol, pcophore, bounds, use2DLimits=False, index=0, soFar=[])¶: Enumerates the list of atom mappings a molecule has to a particular pharmacophore. We do check distance bounds here.

rdkit.Chem.Pharm3D.EmbedLib.DownsampleBoundsMatrix(bm, indices, maxThresh=4.0)¶

Removes rows from a bounds matrix that are that are greater than a threshold value away from a set of other points

Returns the modfied bounds matrix

The goal of this function is to remove rows from the bounds matrix that correspond to atoms (atomic index) that are likely to be quite far from the pharmacophore we’re interested in. Because the bounds smoothing we eventually have to do is N^3, this can be a big win

>>> boundsMat = numpy.array([[0.0, 3.0, 4.0],[2.0, 0.0, 3.0],[2.0, 2.0, 0.0]])
>>> bm = DownsampleBoundsMatrix(boundsMat,(0, ), 3.5)
>>> bm.shape == (2, 2)
True
we don’t touch the input matrix:
>>> boundsMat.shape == (3, 3)
True
>>> print(', '.join([f'{x:.3f}' for x in bm[0]]))
0.000, 3.000
>>> print(', '.join([f'{x:.3f}' for x in bm[1]]))
2.000, 0.000
if the threshold is high enough, we don’t do anything:
>>> boundsMat = numpy.array([[0.0, 4.0, 3.0],[2.0, 0.0, 3.0],[2.0, 2.0, 0.0]])
>>> bm = DownsampleBoundsMatrix(boundsMat, (0, ), 5.0)
>>> bm.shape == (3, 3)
True
If there’s a max value that’s close enough to any of the indices we pass in, we’ll keep it:
>>> boundsMat = numpy.array([[0.0, 4.0, 3.0],[2.0, 0.0, 3.0],[2.0, 2.0, 0.0]])
>>> bm = DownsampleBoundsMatrix(boundsMat, (0, 1), 3.5)
>>> bm.shape == (3, 3)
True
However, the datatype should not be changed or uprank into np.float64 as default behaviour >>> boundsMat = numpy.array([[0.0, 4.0, 3.0],[2.0, 0.0, 3.0],[2.0, 2.0, 0.0]], dtype=numpy.float32) >>> bm = DownsampleBoundsMatrix(boundsMat,(0, 1), 3.5) >>> bm.dtype == numpy.float64 False >>> bm.dtype == numpy.float32 or numpy.issubdtype(bm.dtype, numpy.float32) True >>> bm.dtype == boundsMat.dtype or numpy.issubdtype(bm.dtype, boundsMat.dtype) True

rdkit.Chem.Pharm3D.EmbedLib.EmbedMol(mol, bm, atomMatch=None, weight=2.0, randomSeed=-1, excludedVolumes=None)¶

Generates an embedding for a molecule based on a bounds matrix and adds a conformer (id 0) to the molecule

if the optional argument atomMatch is provided, it will be used to provide supplemental weights for the embedding routine (used in the optimization phase to ensure that the resulting geometry really does satisfy the pharmacophore).

if the excludedVolumes is provided, it should be a sequence of ExcludedVolume objects

>>> m = Chem.MolFromSmiles('c1ccccc1C')
>>> bounds = MolDG.GetMoleculeBoundsMatrix(m)
>>> bounds.shape == (7, 7)
True
>>> m.GetNumConformers()
0
>>> EmbedMol(m,bounds,randomSeed=23)
>>> m.GetNumConformers()
1

rdkit.Chem.Pharm3D.EmbedLib.EmbedOne(mol, name, match, pcophore, count=1, silent=0, **kwargs)¶

generates statistics for a molecule’s embeddings

Four energies are computed for each embedding:

E1: the energy (with constraints) of the initial embedding
E2: the energy (with constraints) of the optimized embedding
E3: the energy (no constraints) the geometry for E2
E4: the energy (no constraints) of the optimized free-molecule (starting from the E3 geometry)

Returns a 9-tuple:

the mean value of E1
the sample standard deviation of E1
the mean value of E2
the sample standard deviation of E2
the mean value of E3
the sample standard deviation of E3
the mean value of E4
the sample standard deviation of E4
The number of embeddings that failed

rdkit.Chem.Pharm3D.EmbedLib.EmbedPharmacophore(mol, atomMatch, pcophore, randomSeed=-1, count=10, smoothFirst=True, silent=False, bounds=None, excludedVolumes=None, targetNumber=-1, useDirs=False)¶

Generates one or more embeddings for a molecule that satisfy a pharmacophore

atomMatch is a sequence of sequences containing atom indices for each of the pharmacophore’s features.

count: is the maximum number of attempts to make a generating an embedding

smoothFirst: toggles triangle smoothing of the molecular bounds matix

bounds: if provided, should be the molecular bounds matrix. If this isn’t
provided, the matrix will be generated.

targetNumber: if this number is positive, it provides a maximum number
of embeddings to generate (i.e. we’ll have count attempts to generate targetNumber embeddings).

returns: a 3 tuple:

the molecular bounds matrix adjusted for the pharmacophore
a list of embeddings (molecules with a single conformer)
the number of failed attempts at embedding

>>> from rdkit import Geometry
>>> from rdkit.Chem.Pharm3D import Pharmacophore
>>> m = Chem.MolFromSmiles('OCCN')
>>> feats = [
...   ChemicalFeatures.FreeChemicalFeature('HBondAcceptor', 'HAcceptor1',
...                                        Geometry.Point3D(0.0, 0.0, 0.0)),
...   ChemicalFeatures.FreeChemicalFeature('HBondDonor', 'HDonor1',
...                                        Geometry.Point3D(2.65, 0.0, 0.0)),
...   ]
>>> pcophore=Pharmacophore.Pharmacophore(feats)
>>> pcophore.setLowerBound(0,1, 2.5)
>>> pcophore.setUpperBound(0,1, 3.5)
>>> atomMatch = ((0, ), (3, ))

>>> bm,embeds,nFail = EmbedPharmacophore(m, atomMatch, pcophore, randomSeed=23, silent=1)
>>> len(embeds)
10
>>> nFail
0

Set up a case that can’t succeed:

>>> pcophore = Pharmacophore.Pharmacophore(feats)
>>> pcophore.setLowerBound(0,1, 2.0)
>>> pcophore.setUpperBound(0,1, 2.1)
>>> atomMatch = ((0, ), (3, ))

>>> bm, embeds, nFail = EmbedPharmacophore(m, atomMatch, pcophore, randomSeed=23, silent=1)
>>> len(embeds)
0
>>> nFail
10

rdkit.Chem.Pharm3D.EmbedLib.GetAllPharmacophoreMatches(matches, bounds, pcophore, useDownsampling=0, progressCallback=None, use2DLimits=False, mol=None, verbose=False)¶

rdkit.Chem.Pharm3D.EmbedLib.GetAtomHeavyNeighbors(atom)¶

returns a list of the heavy-atom neighbors of the atom passed in:

>>> m = Chem.MolFromSmiles('CCO')
>>> l = GetAtomHeavyNeighbors(m.GetAtomWithIdx(0))
>>> len(l)
1
>>> isinstance(l[0],Chem.Atom)
True
>>> l[0].GetIdx()
1

>>> l = GetAtomHeavyNeighbors(m.GetAtomWithIdx(1))
>>> len(l)
2
>>> l[0].GetIdx()
0
>>> l[1].GetIdx()
2

rdkit.Chem.Pharm3D.EmbedLib.MatchFeatsToMol(mol, featFactory, features)¶

generates a list of all possible mappings of each feature to a molecule

Returns a 2-tuple:

a boolean indicating whether or not all features were found
a list, numFeatures long, of sequences of features

>>> import os.path
>>> from rdkit import RDConfig, Geometry
>>> fdefFile = os.path.join(RDConfig.RDCodeDir, 'Chem/Pharm3D/test_data/BaseFeatures.fdef')
>>> featFactory = ChemicalFeatures.BuildFeatureFactory(fdefFile)
>>> activeFeats = [
...  ChemicalFeatures.FreeChemicalFeature('Acceptor', Geometry.Point3D(0.0, 0.0, 0.0)),
...  ChemicalFeatures.FreeChemicalFeature('Donor', Geometry.Point3D(0.0, 0.0, 0.0))]
>>> m = Chem.MolFromSmiles('FCCN')
>>> match, mList = MatchFeatsToMol(m, featFactory, activeFeats)
>>> match
True

Two feature types:

>>> len(mList)
2

The first feature type, Acceptor, has two matches:

>>> len(mList[0])
2
>>> mList[0][0].GetAtomIds()
(0,)
>>> mList[0][1].GetAtomIds()
(3,)

The first feature type, Donor, has a single match:

>>> len(mList[1])
1
>>> mList[1][0].GetAtomIds()
(3,)

rdkit.Chem.Pharm3D.EmbedLib.MatchPharmacophore(matches, bounds, pcophore, useDownsampling=False, use2DLimits=False, mol=None, excludedVolumes=None, useDirs=False)¶: if use2DLimits is set, the molecule must also be provided and topological distances will also be used to filter out matches

rdkit.Chem.Pharm3D.EmbedLib.MatchPharmacophoreToMol(mol, featFactory, pcophore)¶

generates a list of all possible mappings of a pharmacophore to a molecule

Returns a 2-tuple:

a boolean indicating whether or not all features were found
a list, numFeatures long, of sequences of features

>>> import os.path
>>> from rdkit import Geometry, RDConfig
>>> from rdkit.Chem.Pharm3D import Pharmacophore
>>> fdefFile = os.path.join(RDConfig.RDCodeDir,'Chem/Pharm3D/test_data/BaseFeatures.fdef')
>>> featFactory = ChemicalFeatures.BuildFeatureFactory(fdefFile)
>>> activeFeats = [
...  ChemicalFeatures.FreeChemicalFeature('Acceptor', Geometry.Point3D(0.0, 0.0, 0.0)),
...  ChemicalFeatures.FreeChemicalFeature('Donor',Geometry.Point3D(0.0, 0.0, 0.0))]
>>> pcophore= Pharmacophore.Pharmacophore(activeFeats)
>>> m = Chem.MolFromSmiles('FCCN')
>>> match, mList = MatchPharmacophoreToMol(m,featFactory,pcophore)
>>> match
True

Two feature types:

>>> len(mList)
2

The first feature type, Acceptor, has two matches:

>>> len(mList[0])
2
>>> mList[0][0].GetAtomIds()
(0,)
>>> mList[0][1].GetAtomIds()
(3,)

The first feature type, Donor, has a single match:

>>> len(mList[1])
1
>>> mList[1][0].GetAtomIds()
(3,)

rdkit.Chem.Pharm3D.EmbedLib.OptimizeMol(mol, bm, atomMatches=None, excludedVolumes=None, forceConstant=1200.0, maxPasses=5, verbose=False)¶

carries out a UFF optimization for a molecule optionally subject to the constraints in a bounds matrix

atomMatches, if provided, is a sequence of sequences

forceConstant is the force constant of the spring used to enforce the constraints

returns a 2-tuple:

the energy of the initial conformation
the energy post-embedding

NOTE that these energies include the energies of the constraints

>>> from rdkit import Geometry
>>> from rdkit.Chem.Pharm3D import Pharmacophore
>>> m = Chem.MolFromSmiles('OCCN')
>>> feats = [
...  ChemicalFeatures.FreeChemicalFeature('HBondAcceptor', 'HAcceptor1',
...                                       Geometry.Point3D(0.0, 0.0, 0.0)),
...  ChemicalFeatures.FreeChemicalFeature('HBondDonor', 'HDonor1',
...                                       Geometry.Point3D(2.65, 0.0, 0.0)),
...  ]
>>> pcophore=Pharmacophore.Pharmacophore(feats)
>>> pcophore.setLowerBound(0,1, 2.5)
>>> pcophore.setUpperBound(0,1, 2.8)
>>> atomMatch = ((0, ), (3, ))
>>> bm, embeds, nFail = EmbedPharmacophore(m, atomMatch, pcophore, randomSeed=23, silent=1)
>>> len(embeds)
10
>>> testM = embeds[0]

Do the optimization:

>>> e1, e2 = OptimizeMol(testM,bm,atomMatches=atomMatch)

Optimizing should have lowered the energy:

>>> e2 < e1
True

Check the constrained distance:

>>> conf = testM.GetConformer(0)
>>> p0 = conf.GetAtomPosition(0)
>>> p3 = conf.GetAtomPosition(3)
>>> d03 = p0.Distance(p3)
>>> bool(d03 >= pcophore.getLowerBound(0,1) - 0.01)
True
>>> bool(d03 <= pcophore.getUpperBound(0,1) + 0.01)
True

If we optimize without the distance constraints (provided via the atomMatches argument) we’re not guaranteed to get the same results, particularly in a case like the current one where the pharmacophore brings the atoms uncomfortably close together:

>>> testM = embeds[1]
>>> e1, e2 = OptimizeMol(testM,bm)
>>> e2 < e1
True
>>> conf = testM.GetConformer(0)
>>> p0 = conf.GetAtomPosition(0)
>>> p3 = conf.GetAtomPosition(3)
>>> d03 = p0.Distance(p3)
>>> bool(d03 >= pcophore.getLowerBound(0, 1) - 0.01)
True
>>> bool(d03 <= pcophore.getUpperBound(0, 1) + 0.01)
False

rdkit.Chem.Pharm3D.EmbedLib.ReplaceGroup(match, bounds, slop=0.01, useDirs=False, dirLength=2.0)¶

Adds an entry at the end of the bounds matrix for a point at the center of a multi-point feature

returns a 2-tuple:: new bounds mat index of point added

>>> boundsMat = numpy.array([[0.0, 2.0, 2.0],[1.0, 0.0, 2.0],[1.0, 1.0, 0.0]])
>>> match = [0, 1, 2]
>>> bm,idx = ReplaceGroup(match, boundsMat, slop=0.0)

the index is at the end:

>>> idx == 3
True

and the matrix is one bigger:

>>> bm.shape == (4, 4)
True

but the original bounds mat is not altered:

>>> boundsMat.shape == (3, 3)
True

We make the assumption that the points of the feature form a regular polygon, are listed in order (i.e. pt 0 is a neighbor to pt 1 and pt N-1) and that the replacement point goes at the center:

>>> print(', '.join([f'{x:.3f}' for x in bm[-1]]))
0.577, 0.577, 0.577, 0.000
>>> print(', '.join([f'{x:.3f}' for x in bm[:,-1]]))
1.155, 1.155, 1.155, 0.000

The slop argument (default = 0.01) is fractional:

>>> bm, idx = ReplaceGroup(match, boundsMat)
>>> print(', '.join([f'{x:.3f}' for x in bm[-1]]))
0.572, 0.572, 0.572, 0.000
>>> print(', '.join([f'{x:.3f}' for x in bm[:,-1]]))
1.166, 1.166, 1.166, 0.000

rdkit.Chem.Pharm3D.EmbedLib.UpdatePharmacophoreBounds(bm, atomMatch, pcophore, useDirs=False, dirLength=2.0, mol=None)¶

loops over a distance bounds matrix and replaces the elements that are altered by a pharmacophore

NOTE this returns the resulting bounds matrix, but it may also alter the input matrix

atomMatch is a sequence of sequences containing atom indices for each of the pharmacophore’s features.

>>> from rdkit import Geometry
>>> from rdkit.Chem.Pharm3D import Pharmacophore
>>> feats = [
...   ChemicalFeatures.FreeChemicalFeature('HBondAcceptor', 'HAcceptor1',
...                                        Geometry.Point3D(0.0, 0.0, 0.0)),
...   ChemicalFeatures.FreeChemicalFeature('HBondDonor', 'HDonor1',
...                                        Geometry.Point3D(2.65, 0.0, 0.0)),
...   ]
>>> pcophore = Pharmacophore.Pharmacophore(feats)
>>> pcophore.setLowerBound(0,1, 1.0)
>>> pcophore.setUpperBound(0,1, 2.0)

>>> boundsMat = numpy.array([[0.0, 3.0, 3.0],[2.0, 0.0, 3.0],[2.0, 2.0, 0.0]])
>>> atomMatch = ((0, ), (1, ))
>>> bm = UpdatePharmacophoreBounds(boundsMat, atomMatch, pcophore)

In this case, there are no multi-atom features, so the result matrix is the same as the input:
>>> bm is boundsMat
True
this means, of course, that the input boundsMat is altered:
>>> print(', '.join([f'{x:.3f}' for x in boundsMat[0]]))
0.000, 2.000, 3.000
>>> print(', '.join([f'{x:.3f}' for x in boundsMat[1]]))
1.000, 0.000, 3.000
>>> print(', '.join([f'{x:.3f}' for x in boundsMat[2]]))
2.000, 2.000, 0.000

rdkit.Chem.Pharm3D.EmbedLib.isNaN(v)¶

provides an OS independent way of detecting NaNs This is intended to be used with values returned from the C++ side of things.

We can’t actually test this from Python (which traps zero division errors), but it would work something like this if we could:

>>> isNaN(0)
False

#>>> isNan(1/0) #True

rdkit.Chem.Pharm3D.EmbedLib module¶

Table of Contents

Previous topic

Next topic

This Page