aizynthfinder.analysis package

Submodules

aizynthfinder.analysis.routes module

Module containing classes to store and manipulate collections of synthetic routes.

class aizynthfinder.analysis.routes.RouteCollection(reaction_trees, **kwargs)

Bases: object

Holds a collections of reaction routes.

If can be the top scored nodes, their scores and the reaction trees created from them. It can also be a cluster of such routes.

The class has the functionality to compute collective results for the different routes such as images.

Properties of individual route can be obtained with simple indexing.

route0 = collection[0]
Variables

  • all_scores – all the computed scores for the routes

  • nodes – the top-ranked MCTS-like nodes

  • scores – initial scores of top-ranked nodes or routes

  • reaction_trees – the reaction trees created from the top-ranked nodes

  • clusters – the created clusters from the collection

  • route_metadata – the metadata of the reaction trees

Parameters

reaction_trees (Sequence[ReactionTree]) – the trees to base the collection on

Return type

None

classmethod from_analysis(analysis, selection=None)

Create a collection from a tree analysis.

Parameters
Returns

the created collection

Return type

aizynthfinder.analysis.routes.RouteCollection

property dicts: Sequence[StrDict]

Returns a list of dictionary representation of the routes

property images: Sequence[PilImage]

Returns a list of pictoral representation of the routes

property jsons: Sequence[str]

Returns a list of JSON string representation of the routes

cluster(n_clusters, max_clusters=5, distances_model='ted', **kwargs)

Cluster the route collection into a number of clusters.

Additional arguments to the distance or clustering algorithm can be passed in as key-word arguments.

When distances_model is “lstm”, a key-word argument model_path needs to be given when distances_model is “ted”, two optional key-word arguments timeout and content can be given.

If the number of reaction trees are less than 3, no clustering will be performed

Parameters

  • n_clusters (int) – the desired number of clusters, if less than 2 triggers optimization

  • max_clusters (int) – the maximum number of clusters to consider

  • distances_model (str) – can be ted or lstm and determines how the route distances are computed

  • kwargs (Any) –

Returns

the cluster labels

Return type

np.ndarray

combined_reaction_trees(recreate=False)

Return an object that combines all the reaction tree into a single reaction tree graph

Parameters

recreate (bool) – if False will return a cached object if available, defaults to False

Returns

the combined trees

Return type

aizynthfinder.analysis.utils.CombinedReactionTrees

compute_scores(*scorers)

Compute new scores for all routes in this collection. They can then be accessed with the all_scores attribute.

Parameters

scorers (Scorer) –

Return type

None

dict_with_extra(include_scores=False, include_metadata=False)

Return the routes as dictionaries with optionally all scores and all metadata added to the root (target) node.

Returns

the routes as dictionaries

Return type

Sequence[StrDict]

dict_with_scores()

Return the routes as dictionaries with all scores added to the root (target) node.

Returns

the routes as dictionaries

Return type

Sequence[StrDict]

distance_matrix(recreate=False, model='ted', **kwargs)

Compute the distance matrix between each pair of reaction trees

All key-word arguments are passed along to the route_distance_calculator function from the route_distances package.

When model is “lstm”, a key-word argument model_path needs to be given when model is “ted”, two optional key-word arguments timeout and content can be given.

Parameters

  • recreate (bool) – if False, use a cached one if available

  • model (str) – the type of model to use “ted” or “lstm”

  • kwargs (Any) –

Returns

the square distance matrix

Return type

np.ndarray

make_dicts()

Convert all reaction trees to dictionaries

Return type

Sequence[StrDict]

make_images()

Convert all reaction trees to images

Return type

Sequence[Optional[PilImage]]

make_jsons()

Convert all reaction trees to JSON strings

Return type

Sequence[str]

rescore(scorer)

Rescore the routes in the collection, and thereby re-order them.

This will replace the scores attribute, and update the all_scores attribute with another entry.

Parameters

scorer (Scorer) – the scorer to use

Return type

None

aizynthfinder.analysis.tree_analysis module

Module containing classes to perform analysis of the tree search results.

class aizynthfinder.analysis.tree_analysis.TreeAnalysis(search_tree, scorer=None)

Bases: object

Class that encapsulate various analysis that can be performed on a search tree.

Variables

  • scorer – the object used to score the nodes

  • search_tree – the search tree

Parameters
Return type

None

best()

Returns the route or MCTS-like node with the highest score. If several routes have the same score, it will return the first

Returns

the top scoring node or route

Return type

Union[MctsNode, ReactionTree]

sort(selection=None)

Sort and select the nodes or routes in the search tree.

Parameters

selection (Optional[RouteSelectionArguments]) – selection criteria for the routes

Returns

the items

Returns

the score

Return type

Tuple[Union[Sequence[MctsNode], Sequence[ReactionTree]], Sequence[float]]

tree_statistics()

Returns statistics of the tree

Currently it returns the number of nodes, the maximum number of transforms, maximum number of children, top score, if the top score route is solved, the number of molecule in the top score node, and information on pre-cursors

Returns

the statistics

Return type

StrDict

aizynthfinder.analysis.utils module

Helper routines and class for the aizynthfinder.analysis package. To avoid clutter in that package, larger utility algorithms are placed herein.

class aizynthfinder.analysis.utils.RouteSelectionArguments(nmin=5, nmax=25, return_all=False)

Bases: object

Selection arguments for the tree analysis class

If return_all is False, it will return at least nmin routes and if routes have the same score it will return them as well up to nmax routes.

If return_all is True, it will return all solved routes if there is at least one is solved, otherwise the nmin and nmax will be used.

Parameters

  • nmin (int) –

  • nmax (int) –

  • return_all (bool) –

Return type

None

nmin: int = 5
nmax: int = 25
return_all: bool = False
class aizynthfinder.analysis.utils.CombinedReactionTrees(reaction_trees)

Bases: object

Encapsulation of an algorithm that combines several reaction trees into a larger bipartite graph with all reactions and molecules.

The reactions at a specific level of the reaction trees are grouped based on the reaction smiles.

Params reactions_trees

the list of reaction trees to combine

Parameters

reaction_trees (Sequence[ReactionTree]) –

Return type

None

to_dict()

Returns the graph as a dictionary in a pre-defined format.

Returns

the combined reaction trees

Return type

StrDict

to_visjs_page(filename, in_stock_colors=None)

Create a visualization of the combined reaction tree using the vis.js network library.

The HTML page and all the images will be put into a tar-ball.

Parameters

  • filename (str) – the name of the tarball

  • in_stock_colors (Optional[FrameColors]) – the colors around molecules, defaults to {True: “green”, False: “orange”}

Return type

None

Module contents

Sub-package containing analysis routines