1.1.1.2.1.1. molpher.algorithms.antidecoys package

1.1.1.2.1.1.1. Submodules

1.1.1.2.1.1.2. molpher.algorithms.antidecoys.custom_opers module

Custom operations and callbacks used in AntidecoysPathFinder.

class molpher.algorithms.antidecoys.custom_opers.AntiFpSortCallback(antifp_scores)[source]

Bases: molpher.swig_wrappers.core.SortMorphsCallback

Parameters:antifp_scores (dict) – SMILES to anti-fingerprint similarity map (for each molecule in candidates)

This callback is used to sort candidates with regard to their similarity to the anti-fingerprint. Molecules less similar to the anti-fingerprint are ranked higher than more similar ones.

antifp_scores = None

SMILES to anti-fingerprint score map (for each molecule in candidates)

maximum_common_bits_perc = None

maximum percentage of bits that one candidate morph has in common with the anti-fingerprint

minimum_common_bits_perc = None

minimum percentage of bits that one candidate morph has in common with the anti-fingerprint

class molpher.algorithms.antidecoys.custom_opers.AntidecoysFilter(scores, common_bits_thrs, minimum_accept)[source]

Bases: molpher.core.operations.TreeOperation.TreeOperation

Parameters:
  • scores – a dict mapping a SMILES string of a molecule in candidates to the corresponding score value
  • common_bits_thrs – maximum percentage of bits in common with the anti-fingerprint allowed in the
  • minimum_accept – always accept at least this many top scoring molecules

A filtering tree operation which filters the morphs in candidates based on common_bits_thrs. All morphs with anti-fingerprint score bigger than common_bits_thrs are rejected. However, at least minimum_accept best morphs survives.

common_bits_thrs = None

maximum percentage of bits in common with the anti-fingerprint allowed in the

minimum_accept = None

always accept at least this many top scoring molecules

scores = None

a dict mapping a SMILES string of a molecule in candidates to the corresponding score value

class molpher.algorithms.antidecoys.custom_opers.GatherAntiFPScores(out, anti_fp, settings)[source]

Bases: molpher.core.operations.TreeOperation.TreeOperation

Parameters:
  • out – a dictionary to save the results to
  • anti_fp – the anti-fingerprint
  • settings – AntidecoysSettings

A tree operation which computes the anti-fingerprint scores using the given settings and anti-fingerprint. For every molecule in candidates the similarity to the anti-fingerprint is computed and saved as a score into the supplied dictionary.

See also

compute_antifp_scores

antitfp = None

the anti-fingerprint

out = None

output dictionary

settings = None

AntidecoysSettings

1.1.1.2.1.1.3. molpher.algorithms.antidecoys.pathfinder module

class molpher.algorithms.antidecoys.pathfinder.AntidecoysPathFinder(settings, antifingerprint=None)[source]

Bases: object

Parameters:
  • settings (AntidecoysSettings) – exploration settings and parameters
  • antifingerprint (rdkit.cDataStructs.SparseBitVector) – the anti-fingerprint to use (optional)

Implements a modified version of the bidirectional algorithm. The algorithm tries to avoid some already sampled areas of chemical space by using an ‘anti-fingerprint’, a 2D pharmacophore fingerprint that describes previously explored pharmacophore features and their geometric relationships.

The RDKit implementation of pharmacophore fingerprints is used. Therefore, it is necessary to have RDKit installed to use this class and this module.

If no antifingerprint is specified, the search defaults to an ordinary bidirectional algorithm.

__call__()[source]

Execute the search

Returns:list of SMILES strings representing the path found
__dict__ = mappingproxy({'__module__': 'molpher.algorithms.antidecoys.pathfinder', '__doc__': "\n :param settings: exploration settings and parameters\n :type settings: `AntidecoysSettings`\n :param antifingerprint: the anti-fingerprint to use (optional)\n :type antifingerprint: :class:`rdkit.cDataStructs.SparseBitVector`\n\n Implements a modified version of the bidirectional algorithm. The algorithm tries to avoid\n some already sampled areas of chemical space by using an 'anti-fingerprint',\n a 2D pharmacophore fingerprint that describes previously explored pharmacophore features and their geometric relationships.\n\n The `RDKit <http://www.rdkit.org/docs/RDKit_Book.html#representation-of-pharmacophore-fingerprints>`_\n implementation of pharmacophore fingerprints is used. Therefore, it is necessary to have RDKit installed\n to use this class and this module.\n\n If no :samp:`antifingerprint` is specified, the search defaults to an ordinary :mod:`~molpher.algorithms.bidirectional` algorithm.\n\n ", '__init__': <function AntidecoysPathFinder.__init__>, '__call__': <function AntidecoysPathFinder.__call__>, '__dict__': <attribute '__dict__' of 'AntidecoysPathFinder' objects>, '__weakref__': <attribute '__weakref__' of 'AntidecoysPathFinder' objects>})
__init__(settings, antifingerprint=None)[source]

Initialize self. See help(type(self)) for accurate signature.

__module__ = 'molpher.algorithms.antidecoys.pathfinder'
__weakref__

list of weak references to the object (if defined)

antifingerprint = None

the anti-fingerprint

connecting_molecule = None

SMILES string of a molecule that connects the two trees

path = None

a list of SMILES strings representing the found path (defaults to an empty list)

settings = None

AntidecoysSettings object used to initialize this instance

source = None

SMILES of the source

source_target = None

tree searching from source to target

source_target_min = None

FindClosest holding the current minimum in the ‘source to target’ tree

target = None

SMILES of the target

target_source = None

tree searching from target to source

target_source_min = None

FindClosest holding the current minimum in the ‘target to source’ tree

verbose = None

use verbose output

1.1.1.2.1.1.4. molpher.algorithms.antidecoys.run module

molpher.algorithms.antidecoys.run.run(settings, paths_count)[source]
Parameters:
  • settings – instance of AntidecoysSettings specifying search parameters
  • paths_count – number of paths to generate
Returns:

a list of lists of SMILES strings (each path is represented by a list of SMILES strings)

1.1.1.2.1.1.5. molpher.algorithms.antidecoys.settings module

class molpher.algorithms.antidecoys.settings.AntidecoysSettings(source, target, storage_dir='/home/sichom/projects/molpher-lib/doc/antidecoys_data', max_threads=None, tree_params=None, max_iters=100, verbose=False, fg_bins=((0, 2), (2, 5), (5, 8)), fg_min_points=2, fg_max_points=3, min_accepted=1000, common_bits_max_thrs=0.75, common_bits_mean_thrs=0.5, antidecoys_min_iters=10, antidecoys_max_iters=50, distance_thrs=0.2)[source]

Bases: molpher.algorithms.settings.Settings

A specialized class that holds the settings for the anti-decoys algorithm.

antidecoys_max_iters = None

maximum number of iterations that will use the antidecoys filter

antidecoys_min_iters = None

minimum number of iterations that will use the antidecoys filter

common_bits_max_thrs = None

maximum percentage of shared bits between a structure and the anti-fingerprint that the filter will accept on every iteration

common_bits_mean_thrs = None

if for the structures that survived the filter the mean common bits percentage falls below this value, antidecoys will be turned off

distance_thrs = None

turn antidecoys filter off when the distance between two closest molecules from each tree gets below this value

fg_bins = None

distance bins in the pharmacophore fingerprint (as described here)

fg_max_points = None

max number of features encoded in the pharmacophore fingerprint (as described here)

fg_min_points = None

min number of features encoded in the pharmacophore fingerprint (as described here)

min_accepted = None

minimum number of morphs the antidecoys filter will accept on every iteration

1.1.1.2.1.1.6. molpher.algorithms.antidecoys.utils module

molpher.algorithms.antidecoys.utils.compute_anti_fp(mols_smiles, sig_fac, antifp_old=None)[source]

Computes an anti-fingerprint from the given molecules.

It is possible to specify an existing anti-fingerprint for an update. In this case, the returned fingerprint will be the result of doing a bitwise or between the old fingerprint and the one generated from the supplied structures.

Parameters:
  • mols_smiles – SMILES of the molecules to generate the anti-fingerprint from
  • sig_fac – RDKit’s signature factory used in the 2D pharmacophore fingerprint computation
  • antifp_old – an old anti-fingerprint to update
Returns:

new or updated anti-fingerprint
molpher.algorithms.antidecoys.utils.compute_antifp_scores(mols, anti_fp, settings)[source]

Computes (in parallel) the anti-fingerprint scores for the given molecules.

Parameters:
  • mols – molecules as a list of SMILES strings
  • anti_fp – the anti-fingerprint
  • settings – AntidecoysSettings
Returns:

a dict mapping SMILES of the given molecules to the anti-fingerprint scores
molpher.algorithms.antidecoys.utils.update_target(tree, target)[source]

Change the tree’s target molecule.

Parameters:

1.1.1.2.1.1.7. Module contents