aizynthfinder.context.policy package

Submodules

aizynthfinder.context.policy.expansion_strategies module

Module containing classes that implements different expansion policy strategies

class aizynthfinder.context.policy.expansion_strategies.ExpansionStrategy(key, config, **kwargs)

Bases: abc.ABC

A base class for all expansion strategies.

The strategy can be used by either calling the get_actions method of by calling the instantiated class with a list of molecule.

expander = MyExpansionStrategy("dummy", config)
actions, priors = expander.get_actions(molecules)
actions, priors = expander(molecules)
Parameters

  • key (str) – the key or label

  • config (Configuration) – the configuration of the tree search

  • kwargs (str) –

Return type

None

abstract get_actions(molecules, cache_molecules=None)

Get all the probable actions of a set of molecules

Parameters

  • molecules (Sequence[TreeMolecule]) – the molecules to consider

  • cache_molecules (Sequence[TreeMolecule]) – additional molecules to submit to the expansion policy but that only will be cached for later use

Returns

the actions and the priors of those actions

Return type

Tuple[List[RetroReaction], List[float]]

reset_cache()

Reset the prediction cache

Return type

None

class aizynthfinder.context.policy.expansion_strategies.MultiExpansionStrategy(key, config, **kwargs)

Bases: aizynthfinder.context.policy.expansion_strategies.ExpansionStrategy

A base class for combining multiple expansion strategies.

The strategy can be used by either calling the get_actions method or by calling the instantiated class with a list of molecules.

Variables

  • expansion_strategy_keys – the keys of the selected expansion strategies

  • additive_expansion – a conditional setting to specify whether all the actions and priors of the selected expansion strategies should be combined or not. Defaults to False.

Parameters

  • key (str) – the key or label

  • config (Configuration) – the configuration of the tree search

  • expansion_strategies – the keys of the selected expansion strategies. All keys of the selected expansion strategies must exist in the expansion policies listed in config

  • kwargs (str) –

Return type

None

get_actions(molecules, cache_molecules=None)

Get all the probable actions of a set of molecules, using the selected policies.

The default implementation combines all the actions and priors of the selected expansion strategies into two lists respectively if the ‘additive_expansion’ setting is set to True. This function can be overridden by a sub class to combine different expansion strategies in different ways.

Parameters
Returns

the actions and the priors of those actions

Raises

PolicyException: if the policy isn’t selected

Return type

Tuple[List[RetroReaction], List[float]]

class aizynthfinder.context.policy.expansion_strategies.TemplateBasedExpansionStrategy(key, config, **kwargs)

Bases: aizynthfinder.context.policy.expansion_strategies.ExpansionStrategy

A template-based expansion strategy that will return TemplatedRetroReaction objects upon expansion.

Variables

  • template_column – the column in the template file that contains the templates

  • cutoff_cumulative – the accumulative probability of the suggested templates

  • cutoff_number – the maximum number of templates to returned

  • use_rdchiral – a boolean to apply templates with RDChiral

  • use_remote_models – a boolean to connect to remote TensorFlow servers

  • rescale_prior – a boolean to apply softmax to the priors

  • chiral_fingerprints – if True will base expansion on chiral fingerprint

Parameters

  • key (str) – the key or label

  • config (Configuration) – the configuration of the tree search

  • model – the source of the policy model

  • template – the path to a HDF5 file with the templates

  • kwargs (str) –

Raises

PolicyException – if the length of the model output vector is not same as the number of templates

Return type

None

get_actions(molecules, cache_molecules=None)

Get all the probable actions of a set of molecules, using the selected policies and given cutoffs

Parameters

  • molecules (Sequence[TreeMolecule]) – the molecules to consider

  • cache_molecules (Sequence[TreeMolecule]) – additional molecules to submit to the expansion policy but that only will be cached for later use

Returns

the actions and the priors of those actions

Return type

Tuple[List[RetroReaction], List[float]]

reset_cache()

Reset the prediction cache

Return type

None

aizynthfinder.context.policy.filter_strategies module

Module containing classes that implements different filter policy strategies

class aizynthfinder.context.policy.filter_strategies.FilterStrategy(key, config, **kwargs)

Bases: abc.ABC

A base class for all filter strategies.

The filter can be applied by either calling the apply method of by calling the instantiated class with a reaction.

filter = MyFilterStrategy("dummy", config)
filter.apply(reaction)
filter(reaction)
Parameters

  • key (str) – the key or label

  • config (Configuration) – the configuration of the tree search

  • kwargs (Any) –

Return type

None

abstract apply(reaction)

Apply the filter on the reaction. If the reaction should be rejected a RejectionException is raised

Parameters

reaction (RetroReaction) – the reaction to filter

Raises

if the reaction should be rejected.

Return type

None

class aizynthfinder.context.policy.filter_strategies.QuickKerasFilter(key, config, **kwargs)

Bases: aizynthfinder.context.policy.filter_strategies.FilterStrategy

Filter quick-filter trained on artificial negative data

Variables

  • use_remote_models – a boolean to connect to remote TensorFlow servers. Defaults to False.

  • filter_cutoff – the cut-off value

Parameters

  • key (str) – the key or label

  • config (Configuration) – the configuration of the tree search

  • model – the source of the policy model

  • kwargs (Any) –

Return type

None

apply(reaction)

Apply the filter on the reaction. If the reaction should be rejected a RejectionException is raised

Parameters

reaction (RetroReaction) – the reaction to filter

Raises

if the reaction should be rejected.

Return type

None

feasibility(reaction)

Computes if a given reaction is feasible by given the reaction fingerprint to a network model

Parameters

reaction (RetroReaction) – the reaction to query

Returns

if the reaction is feasible

Return type

Tuple[bool, float]

class aizynthfinder.context.policy.filter_strategies.ReactantsCountFilter(key, config, **kwargs)

Bases: aizynthfinder.context.policy.filter_strategies.FilterStrategy

Check that the number of reactants is was expected from the template

Parameters

  • key (str) – the key or label

  • config (Configuration) – the configuration of the tree search

  • kwargs (Any) –

Return type

None

apply(reaction)

Apply the filter on the reaction. If the reaction should be rejected a RejectionException is raised

Parameters

reaction (RetroReaction) – the reaction to filter

Raises

if the reaction should be rejected.

Return type

None

aizynthfinder.context.policy.policies module

Module containing classes that interfaces neural network policies

class aizynthfinder.context.policy.policies.ExpansionPolicy(config)

Bases: aizynthfinder.context.collection.ContextCollection

An abstraction of an expansion policy.

This policy provides actions that can be applied to a molecule

Parameters

config (Configuration) – the configuration of the tree search

Return type

None

get_actions(molecules, cache_molecules=None)

Get all the probable actions of a set of molecules, using the selected policies

Parameters

  • molecules (Sequence[TreeMolecule]) – the molecules to consider

  • cache_molecules (Sequence[TreeMolecule]) – additional molecules that potentially are sent to the expansion model but for which predictions are not returned

Returns

the actions and the priors of those actions

Raises

PolicyException: if the policy isn’t selected

Return type

Tuple[List[RetroReaction], List[float]]

load(source)

Add a pre-initialized expansion strategy object to the policy

Parameters

source (aizynthfinder.context.policy.expansion_strategies.ExpansionStrategy) – the item to add

Return type

None

load_from_config(**config)

Load one or more expansion policy from a configuration

The format should be key:

type: name of the expansion class or custom_package.custom_model.CustomClass model: path_to_model template: path_to_templates other settings or params

or key:

  • path_to_model

  • path_to_templates

Parameters

config (Any) – the configuration

Return type

None

reset_cache()

Reset the cache on all loaded policies

Return type

None

class aizynthfinder.context.policy.policies.FilterPolicy(config)

Bases: aizynthfinder.context.collection.ContextCollection

An abstraction of a filter policy.

This policy provides a query on a reaction to determine whether it should be rejected

Parameters

config (Configuration) – the configuration of the tree search

Return type

None

apply(reaction)

Apply the all the selected filters on the reaction. If the reaction should be rejected a RejectionException is raised

Parameters

reaction (RetroReaction) – the reaction to filter

Raises

if the reaction should be rejected or if a policy is selected

Return type

None

load(source)

Add a pre-initialized filter strategy object to the policy

Parameters

source (aizynthfinder.context.policy.filter_strategies.FilterStrategy) – the item to add

Return type

None

load_from_config(**config)

Load one or more filter policy from a configuration

The format should be key:

type: name of the filter class or custom_package.custom_model.CustomClass model: path_to_model other settings or params

or key: path_to_model

Parameters

config (Any) – the configuration

Return type

None

aizynthfinder.context.policy.utils module

Module containing helper routines for policies

Module contents

Sub-package containing policy routines