Pharmacophore class¶
Class
Main class |
Initialization and setup
Initializes Pharmacophore instance |
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Changes parameters of the pharmacophore instance. |
Create pharmacophore
Creates pharmacophore from RDKit Mol. |
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Creates pharmacophore from RDKit Mol and features encoded by custom SMARTS. |
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Creates pharmacophore from RDKit Mol and features encoded by custom RDKit feature factory. |
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Creates pharmacophore from RDKit Mol and atom ids subsets associated with particular features |
Pharmacophore properties and methods
Returns current binning step value |
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Returns count-based descriptor string of a pharmacophore. |
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Returns coordinates of features. |
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Returns number of each feature type |
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Returns a bitstring fingerprint of a pharmacophore encoded by subsets of features |
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Returns a bitstring fingerprint of a pharmacophore encoded by subsets of features obtained with different setups |
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Returns a copy of a pharmacophore graph |
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Returns a new mirrored Pharmacophore instance. |
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Returns RDKit Mol object of a pharmacophore where features are replaced with atoms |
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Returns pharmacophore hash. |
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Iterates over subsets of features to get their hashes. |
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Iterates over subsets of features created by addition of a single feature to the input list of features. |
Pharmacophore match
Matches the supplied pharmacophore model. |
Load/save file
Reads pharmacophore from a pma-file. |
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Reads pharmacophore from xyz-file. |
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Reads pharmacophore from LigandScout pml-file |
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Saves pharmacophore to LigandScout pml-file. |
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Saves pharmacophore in json format. |
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class
pmapper.pharmacophore.
Pharmacophore
(bin_step=1, cached=False)¶ Main class
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__init__
(bin_step=1, cached=False)¶ Initializes Pharmacophore instance
- Parameters
bin_step (float) – binning step
cached (bool) – whether or not to cache intermediate computation results. This substantially increases speed of repeated computation of a hash or fingerprints.
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fit_model
(model, n_omitted=0, essential_features=None, tol=0, get_transform_matrix=False)¶ Matches the supplied pharmacophore model.
- Parameters
model (Pharmacophore) – a pharmacophore model which is used for matching (it should be a subgraph of the current pharmacophore graph).
n_omitted (int) – a number of simultaneously omitted features in a model pharmacophore
essential_features (list) – a list of ids of features which will not be omitted in a model pharmacophore.
tol (float) – tolerance
get_transform_matrix (bool) – if set, the function will return a transformation matrix as an additional output to align the pharmacopore to a model
- Returns
tuple of feature ids of a model matching the pharmacophore or a 2-tuple where the first item is tuple of feature ids of a model matching the pharmacophore and the seond item is a transformation matrix. If no matching None will be returned.
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get_bin_step
()¶ Returns current binning step value
- Returns
value of a binning step of a pharmacophore
- Return type
float
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get_descriptors
(tol=0)¶ Returns count-based descriptor string of a pharmacophore.
- Parameters
tol (float) – tolerance
- Returns
dictionary where keys are hashes of feature quadruplets and values are counts of identical quadruples
- Return type
dict
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get_feature_coords
(ids=None)¶ Returns coordinates of features.
- Parameters
ids (iterable (int)) – iterable with feature ids to be used
- Returns
list of 2-tuples where each tuple consists of a label and a 3-tuple with coordinates
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get_features_count
()¶ Returns number of each feature type
- Returns
Counter (dict-like) object with feature labels and their feature count of a pharmacophore
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get_fp
(min_features=3, max_features=3, tol=0, nbits=2048, activate_bits=1)¶ Returns a bitstring fingerprint of a pharmacophore encoded by subsets of features
- Parameters
min_features (int) – minimum number of features in a subset
max_features (int) – maximum number of features in a subset
tol (float) – tolerance
nbits (int) – length of a bit string
activate_bits (int) – number of activated bits per feature subset
- Returns
set of numbers of activated bits (bitstring)
- Return type
set
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get_fp2
(min_features=3, max_features=3, tol=(0, ), nbits=(2048, ), activate_bits=(1, ))¶ Returns a bitstring fingerprint of a pharmacophore encoded by subsets of features obtained with different setups
- Parameters
min_features (int) – minimum number of features in a subset
max_features (int) – maximum number of features in a subset
tol (iterable (float)) – iterable with tolerance values
nbits (iterable (int)) – iterable with lengths of a bit string
activate_bits (iterable (int)) – iterable with numbers of activated bits per feature subset
- Returns
dictionary where key is a tuple of (nbits, activate_bits, tol) and value is a set of numbers of activated bits (bitstring)
- Return type
dict
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get_graph
()¶ Returns a copy of a pharmacophore graph
- Returns
a copy of a NetworkX graph object of a pharmacophore
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get_mirror_pharmacophore
()¶ Returns a new mirrored Pharmacophore instance.
- Returns
a new instance of a Pharmacophore class with all features mirrored in yz-plane.
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get_mol
(ids=None)¶ Returns RDKit Mol object of a pharmacophore where features are replaced with atoms
- Parameters
ids (iterable (int)) – iterable with feature ids to be used
- Returns
RDKit RWMol
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get_signature_md5
(ids=None, tol=0)¶ Returns pharmacophore hash.
- Parameters
ids (iterable (int)) – iterable with feature ids to be used to compute pharmacophore hash
tol (float) – tolerance value to ignore small deviation of quadruplets of features from planarity. Minimal angle between an edge and a plane formed by other three features. Quadruplets having at least one angle less than tolerance value are assigned 0 chirality.
- Returns
md5 hash of a pharmacophore
- Return type
str
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iterate_pharm
(min_features=1, max_features=None, tol=0, return_feature_ids=True)¶ Iterates over subsets of features to get their hashes.
- Parameters
min_features (int) – minimum number of features in a subset
max_features (int) – maximum number of features in a subset
tol (float) – tolerance
return_feature_ids (bool) – whether or not return feature ids
- Returns
generator over hashes of feature subsets or over 2-tuples with a hash and a tuple with feature ids.
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iterate_pharm1
(fix_ids, tol=0, return_feature_ids=True)¶ Iterates over subsets of features created by addition of a single feature to the input list of features.
- Parameters
fix_ids (iterable (int)) – iterable with feature ids which will be used as a constant part of enumerated feature subsets
tol (float) – tolerance
return_feature_ids (bool) – whether or not return feature ids
- Returns
generator over hashes of feature subsets or over 2-tuples with a hash and a tuple with feature ids.
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load_from_atom_ids
(mol, atom_features_ids, confId=-1)¶ Creates pharmacophore from RDKit Mol and atom ids subsets associated with particular features
- Parameters
mol – RDKit Mol object
atom_features_ids – dictionary where keys are feature labels and values are lists of tuples with atom ids of individual features, e.g. {‘A’: [(12,), (14,)], ‘H’: [(11,12,13,14,15,16)], …}
confId – id of a conformer in a molecule
- Returns
nothing
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load_from_feature_factory
(mol, factory)¶ Creates pharmacophore from RDKit Mol and features encoded by custom RDKit feature factory.
- Parameters
mol – RDKit Mol object
factory – object of MolChemicalFeatureFactory class
- Returns
nothing
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load_from_file
(fname)¶ Reads pharmacophore from file. File format will be recognized by extension.
- Parameters
fname – file name
- Returns
nothing
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load_from_mol
(mol)¶ Creates pharmacophore from RDKit Mol. Uses default definition of feature SMARTS.
- Parameters
mol – RDKit Mol object
- Returns
nothing
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load_from_pma
(fname)¶ Reads pharmacophore from a pma-file.
- Parameters
fname – pma-file name
- Returns
nothing
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load_from_smarts
(mol, smarts)¶ Creates pharmacophore from RDKit Mol and features encoded by custom SMARTS.
- Parameters
mol – RDKit Mol object
smarts – dictionary of SMARTS of features obtained with load_smarts function from pmapper.util module
- Returns
nothing
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load_from_xyz
(fname)¶ Reads pharmacophore from xyz-file.
- Parameters
fname – xyz-file name
- Returns
nothing
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load_ls_model
(pml_fname)¶ Reads pharmacophore from LigandScout pml-file
- Parameters
pml_fname – file name of a LigandScout pml-file
- Returns
nothing
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save_ls_model
(fname, name='pmapper_pharmcophore')¶ Saves pharmacophore to LigandScout pml-file.
- Parameters
fname – pml-file name
name – name of a pharmacophore which would be stored in a file and will be displayed in LigandScout
- Returns
nothing
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save_to_pma
(fname, feature_ids=None)¶ Saves pharmacophore in json format. This is a native way to store pharmacophore objects in a readable format.
- Parameters
fname – pma-file name
feature_ids – ids of features which should be stored. Default: None (all features).
- Returns
nothing
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update
(bin_step=None, cached=None)¶ Changes parameters of the pharmacophore instance.
- Parameters
bin_step (float) – binning step.
cached (bool) – whether or not to cache intermediate computation results. This substantially increases speed of repeated computation of a hash or fingerprints.
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