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apDict = {}
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ttDict = {}
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Imports: Chem, RDConfig, DataStructs, rdMD, rdmolops, Draw, iteritems, numpy, math, copy, cm
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Calculates the atomic weights for the probe molecule based on a fingerprint function and a metric. Parameters: refMol -- the reference molecule probeMol -- the probe molecule fpFunction -- the fingerprint function metric -- the similarity metric Note: If fpFunction needs additional parameters, use a lambda construct |
Calculates the atomic weights for the probe molecule based on a fingerprint function and the prediction function of a ML model. Parameters: probeMol -- the probe molecule fpFunction -- the fingerprint function predictionFunction -- the prediction function of the ML model |
Normalizes the weights, such that the absolute maximum weight equals 1.0. Parameters: weights -- the list with the atomic weights |
Generates the similarity map for a molecule given the atomic weights. Parameters: mol -- the molecule of interest colorMap -- the matplotlib color map scheme scale -- the scaling: scale < 0 -> the absolute maximum weight is used as maximum scale scale = double -> this is the maximum scale size -- the size of the figure sigma -- the sigma for the Gaussians coordScale -- scaling factor for the coordinates step -- the step for calcAtomGaussian colors -- color of the contour lines contourLines -- if integer number N: N contour lines are drawn if list(numbers): contour lines at these numbers are drawn alpha -- the alpha blending value for the contour lines kwargs -- additional arguments for drawing |
Generates the similarity map for a given reference and probe molecule, fingerprint function and similarity metric. Parameters: refMol -- the reference molecule probeMol -- the probe molecule fpFunction -- the fingerprint function metric -- the similarity metric. kwargs -- additional arguments for drawing |
Generates the similarity map for a given ML model and probe molecule, and fingerprint function. Parameters: probeMol -- the probe molecule fpFunction -- the fingerprint function predictionFunction -- the prediction function of the ML model kwargs -- additional arguments for drawing |
Calculates the atom pairs fingerprint with the torsions of atomId removed. Parameters: mol -- the molecule of interest atomId -- the atom to remove the pairs for (if -1, no pair is removed) fpType -- the type of AP fingerprint ('normal', 'hashed', 'bv') nBits -- the size of the bit vector (only for fpType='bv') minLength -- the minimum path length for an atom pair maxLength -- the maxmimum path length for an atom pair nBitsPerEntry -- the number of bits available for each pair |
Calculates the topological torsion fingerprint with the pairs of atomId removed. Parameters: mol -- the molecule of interest atomId -- the atom to remove the torsions for (if -1, no torsion is removed) fpType -- the type of TT fingerprint ('normal', 'hashed', 'bv') nBits -- the size of the bit vector (only for fpType='bv') minLength -- the minimum path length for an atom pair maxLength -- the maxmimum path length for an atom pair nBitsPerEntry -- the number of bits available for each torsion |
Calculates the Morgan fingerprint with the environments of atomId removed. Parameters: mol -- the molecule of interest radius -- the maximum radius fpType -- the type of Morgan fingerprint: 'count' or 'bv' atomId -- the atom to remove the environments for (if -1, no environments is removed) nBits -- the size of the bit vector (only for fpType = 'bv') useFeatures -- if false: ConnectivityMorgan, if true: FeatureMorgan |
Calculates the RDKit fingerprint with the paths of atomId removed. Parameters: mol -- the molecule of interest atomId -- the atom to remove the paths for (if -1, no path is removed) fpType -- the type of RDKit fingerprint: 'bv' nBits -- the size of the bit vector minPath -- minimum path length maxPath -- maximum path length nBitsPerHash -- number of to set per path |
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