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Imports: RDConfig, sys, time, math, Stats, DG, Chem, numpy, MolDG, ChemicalFeatures, ChemicalForceFields, Pharmacophore, ExcludedVolume, Geometry, logging
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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 |
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(['%.3f'%x for x in bm[-1]])) 0.577, 0.577, 0.577, 0.000 >>> print(', '.join(['%.3f'%x 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(['%.3f'%x for x in bm[-1]])) 0.572, 0.572, 0.572, 0.000 >>> print(', '.join(['%.3f'%x for x in bm[:,-1]])) 1.166, 1.166, 1.166, 0.000 |
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 |
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(['%.3f'%x for x in bm[-1]])) 0.500, 1.500, 1.500, 0.000 >>> print(', '.join(['%.3f'%x for x in bm[:,-1]])) 1.000, 3.000, 3.000, 0.000 |
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. >>> 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(['%.3f'%x for x in boundsMat[0]])) 0.000, 2.000, 3.000 >>> print(', '.join(['%.3f'%x for x in boundsMat[1]])) 1.000, 0.000, 3.000 >>> print(', '.join(['%.3f'%x for x in boundsMat[2]])) 2.000, 2.000, 0.000 |
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: 1) the molecular bounds matrix adjusted for the pharmacophore 2) a list of embeddings (molecules with a single conformer) 3) the number of failed attempts at embedding >>> 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 |
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 |
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: 1) the energy of the initial conformation 2) the energy post-embedding NOTE that these energies include the energies of the constraints >>> 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) >>> d03 >= pcophore.getLowerBound(0,1)-.01 True >>> d03 <= pcophore.getUpperBound(0,1)+.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 pharmcophore 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) >>> d03 >= pcophore.getLowerBound(0,1)-.01 True >>> d03 <= pcophore.getUpperBound(0,1)+.01 False |
generates statistics for a molecule's embeddings Four energies are computed for each embedding: 1) E1: the energy (with constraints) of the initial embedding 2) E2: the energy (with constraints) of the optimized embedding 3) E3: the energy (no constraints) the geometry for E2 4) E4: the energy (no constraints) of the optimized free-molecule (starting from the E3 geometry) Returns a 9-tuple: 1) the mean value of E1 2) the sample standard deviation of E1 3) the mean value of E2 4) the sample standard deviation of E2 5) the mean value of E3 6) the sample standard deviation of E3 7) the mean value of E4 8) the sample standard deviation of E4 9) The number of embeddings that failed |
generates a list of all possible mappings of a pharmacophore to a molecule Returns a 2-tuple: 1) a boolean indicating whether or not all features were found 2) a list, numFeatures long, of sequences of features >>> import os.path >>> dataDir = os.path.join(RDConfig.RDCodeDir,'Chem/Pharm3D/test_data') >>> featFactory = ChemicalFeatures.BuildFeatureFactory(os.path.join(dataDir,'BaseFeatures.fdef')) >>> 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,) |
**INTERNAL USE ONLY** >>> import os.path >>> dataDir = os.path.join(RDConfig.RDCodeDir,'Chem/Pharm3D/test_data') >>> featFactory = ChemicalFeatures.BuildFeatureFactory(os.path.join(dataDir,'BaseFeatures.fdef')) >>> 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') >>> d =_getFeatDict(m,featFactory,activeFeats) >>> sorted(list(d.keys())) ['Acceptor', 'Donor'] >>> donors = d['Donor'] >>> len(donors) 1 >>> donors[0].GetAtomIds() (3,) >>> acceptors = d['Acceptor'] >>> len(acceptors) 2 >>> acceptors[0].GetAtomIds() (0,) >>> acceptors[1].GetAtomIds() (3,) |
generates a list of all possible mappings of each feature to a molecule Returns a 2-tuple: 1) a boolean indicating whether or not all features were found 2) a list, numFeatures long, of sequences of features >>> import os.path >>> dataDir = os.path.join(RDConfig.RDCodeDir,'Chem/Pharm3D/test_data') >>> featFactory = ChemicalFeatures.BuildFeatureFactory(os.path.join(dataDir,'BaseFeatures.fdef')) >>> 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,) |
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]] |
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 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(['%.3f'%x for x in bm[0]])) 0.000, 3.000 >>> print(', '.join(['%.3f'%x 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 |
>>> 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]],numpy.float) >>> 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]],numpy.float) >>> CoarseScreenPharmacophore(((0,),(1,),(2,),(3,)),bounds,pcophore) True >>> CoarseScreenPharmacophore(((0,),(1,),(3,),(2,)),bounds,pcophore) True >>> CoarseScreenPharmacophore(((1,),(0,),(3,),(2,)),bounds,pcophore) False |
checks to see if a particular mapping of features onto a molecule satisfies a pharmacophore's 2D restrictions >>> 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 |
**INTERNAL USE ONLY** checks whether a particular atom match can be satisfied by a molecule |
Enumerates the list of atom mappings a molecule has to a particular pharmacophore. We do check distance bounds here. |
if use2DLimits is set, the molecule must also be provided and topological distances will also be used to filter out matches |
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 >>> 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 |
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