Merge pull request #156 from MolecularAI/release-4.3.0

Release 4.3.0

CHANGELOG.md

@@ -1,5 +1,31 @@
 # CHANGELOG
 
+## Version 4.3.0 2024-05-22
+
+### Features
+
+- Focussed bonds feature has been created to allow users to choose bonds to freeze and break.
+- Functionality has been added to run the broken bonds scorer with MCTS.
+- A disconnection-aware Chemformer expansion strategy has been introduced in the plugins.
+- Weights for single expansion policies can now be provided as input through the config file.
+- The priors returned from the multi-expansion strategy have been rescaled.
+- Functionality to mask reaction templates is now supported in the `TemplateBasedExpansionStrategy`.
+- The `TemplateBasedDirectExpansionStrategy` has been implemented to directly apply the template in the search process.
+- Added support for the C++ version of RDChiral.
+- The multi-objective MCTS core algorithm is now implemented within the MCTS search functionality.
+- A separate GUI component has been introduced for doing MO tree analysis. Additional functionalities have been added to GUI widgets to set two rewards/objectives to MCTS search. Pareto front is automatically plotted if MO-MCTS is run. Route re-ordering is automatically disabled for MO-MCTS.
+- Preprocessing of the tree search can now be done using `aizynthcli`.
+- The `StockAvailablityScorer` has been updated such that it takes and additional `other_source_score` parameter.
+- A `cutoff_number` parameter can be provided to the multi-expansion strategy to obtain only the top predictions.
+- A `BrokenBondsScorer` has been created for scoring nodes and reaction trees based on the breaking of atom bonds.
+- A `RouteSimilarityScorer` has been created for scoring based on an LSTM model for computing Tree Edit Distance to a set of reference routes.
+- A `DeltaSyntheticComplexityScorer` has been created for scoring nodes based on the delta-synthetic-complexity of the node and its parent 'horizon' steps up in the tree.
+
+### Bug-fixes
+
+- Fixed an issue of sending multiple fingerprints to the GPCR Tensorflow serving model.
+- `aizynthcli` has been fixed after updating with multi-objective analysis such that the tool accurately informs the user if a target is solved or not.
+
 ## Version 4.0.0 2023-11-30
 
 ### Features
@@ -283,7 +309,7 @@
 - Add tools to train filter policy
 - Add logic to prevent cycle forming in MCTS by rejecting creation of parent molecule when expanding
 - Introduce new `context` subpackage that contains the `config`, `stock`, `policy` and `scoring` modules
-- The `Stock`, `ExpansionPolicy`, `FilterPolicy` and `ScorerCollection` classes now has a common interface for selection and loading 
+- The `Stock`, `ExpansionPolicy`, `FilterPolicy` and `ScorerCollection` classes now has a common interface for selection and loading
 - Introduce possibility to remove unsantizable reactions from template library when training
 - Catch exceptions from RDChiral more gracefully
 

README.md

@@ -3,7 +3,7 @@
 [![License](https://img.shields.io/github/license/MolecularAI/aizynthfinder)](https://github.com/MolecularAI/aizynthfinder/blob/master/LICENSE)
 [![Tests](https://github.com/MolecularAI/aizynthfinder/workflows/tests/badge.svg)](https://github.com/MolecularAI/aizynthfinder/actions?workflow=tests)
 [![codecov](https://codecov.io/gh/MolecularAI/aizynthfinder/branch/master/graph/badge.svg)](https://codecov.io/gh/MolecularAI/aizynthfinder)
-[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/python/black) 
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/python/black)
 [![version](https://img.shields.io/github/v/release/MolecularAI/aizynthfinder)](https://github.com/MolecularAI/aizynthfinder/releases)
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MolecularAI/aizynthfinder/blob/master/contrib/notebook.ipynb)
 
@@ -28,7 +28,7 @@ The tool has been developed on a Linux platform, but the software has been teste
 
 First time, execute the following command in a console or an Anaconda prompt
 
-    conda create "python>=3.8,<3.10" -n aizynth-env
+    conda create "python>=3.9,<3.11" -n aizynth-env
 
 To install, activate the environment and install the package using pypi
 
@@ -43,12 +43,12 @@ for a smaller package, without all the functionality, you can also type
 
 First clone the repository using Git.
 
-Then execute the following commands in the root of the repository 
+Then execute the following commands in the root of the repository
 
     conda env create -f env-dev.yml
     conda activate aizynth-dev
     poetry install --all-extras
-    
+
 the `aizynthfinder` package is now installed in editable mode.
 
 
@@ -68,7 +68,7 @@ To use the tool you need
     1. A stock file
     2. A trained expansion policy network
     3. A trained filter policy network (optional)
-    
+
 Such files can be downloaded from [figshare](https://figshare.com/articles/AiZynthFinder_a_fast_robust_and_flexible_open-source_software_for_retrosynthetic_planning/12334577) and [here](https://figshare.com/articles/dataset/A_quick_policy_to_filter_reactions_based_on_feasibility_in_AI-guided_retrosynthetic_planning/13280507) or they can be downloaded automatically using
 
 ```
@@ -91,7 +91,7 @@ Run the tests using:
 The full command run on the CI server is available through an `invoke` command
 
     invoke full-tests
-    
+
  ### Documentation generation
 
 The documentation is generated by Sphinx from hand-written tutorials and docstrings

aizynthfinder/aizynthfinder.py

@@ -15,7 +15,8 @@
 )
 from aizynthfinder.chem import FixedRetroReaction, Molecule, TreeMolecule
 from aizynthfinder.context.config import Configuration
-from aizynthfinder.context.scoring import CombinedScorer
+from aizynthfinder.context.policy import BondFilter
+from aizynthfinder.context.scoring import BrokenBondsScorer, CombinedScorer
 from aizynthfinder.reactiontree import ReactionTreeFromExpansion
 from aizynthfinder.search.andor_trees import AndOrSearchTreeBase
 from aizynthfinder.search.mcts import MctsSearchTree
@@ -82,6 +83,9 @@ def __init__(
         self.search_stats: StrDict = dict()
         self.routes = RouteCollection([])
         self.analysis: Optional[TreeAnalysis] = None
+        self._num_objectives = len(
+            self.config.search.algorithm_config.get("search_rewards", [])
+        )
 
     @property
     def target_smiles(self) -> str:
@@ -107,7 +111,7 @@ def target_mol(self, mol: Molecule) -> None:
     def build_routes(
         self,
         selection: Optional[RouteSelectionArguments] = None,
-        scorer: Optional[str] = None,
+        scorer: Optional[Union[str, List[str]]] = None,
     ) -> None:
         """
         Build reaction routes
@@ -116,18 +120,10 @@ def build_routes(
         to extract results from the tree search.
 
         :param selection: the selection criteria for the routes
-        :param scorer: a reference to the object used to score the nodes
+        :param scorer: a reference to the object used to score the nodes, can be a list
         :raises ValueError: if the search tree not initialized
         """
-
-        scorer = scorer or self.config.post_processing.route_scorer
-
-        if not self.tree:
-            raise ValueError("Search tree not initialized")
-
-        _scorer = self.scorers[scorer]
-
-        self.analysis = TreeAnalysis(self.tree, scorer=_scorer)
+        self.analysis = self._setup_analysis(scorer=scorer)
         config_selection = RouteSelectionArguments(
             nmin=self.config.post_processing.min_routes,
             nmax=self.config.post_processing.max_routes,
@@ -174,9 +170,13 @@ def prepare_tree(self) -> None:
             self.stock.exclude(self.target_mol)
             self._logger.debug("Excluding the target compound from the stock")
 
+        if self.config.search.break_bonds or self.config.search.freeze_bonds:
+            self._setup_focussed_bonds(self.target_mol)
+
         self._setup_search_tree()
         self.analysis = None
         self.routes = RouteCollection([])
+        self.filter_policy.reset_cache()
         self.expansion_policy.reset_cache()
 
     def stock_info(self) -> StrDict:
@@ -249,8 +249,40 @@ def tree_search(self, show_progress: bool = False) -> float:
         self.search_stats["time"] = time_past
         return time_past
 
+    def _setup_focussed_bonds(self, target_mol: Molecule) -> None:
+        """
+        Setup multi-objective scoring function with 'broken bonds'-scorer and
+        add 'frozen bonds'-filter to filter policy.
+
+        :param target_mol: the target molecule.
+        """
+        target_mol = TreeMolecule(smiles=target_mol.smiles, parent=None)
+
+        bond_filter_key = "__finder_bond_filter"
+        if self.config.search.freeze_bonds:
+            if not target_mol.has_all_focussed_bonds(self.config.search.freeze_bonds):
+                raise ValueError("Bonds in 'freeze_bond' must exist in target molecule")
+            bond_filter = BondFilter(bond_filter_key, self.config)
+            self.filter_policy.load(bond_filter)
+            self.filter_policy.select(bond_filter_key, append=True)
+        elif (
+            self.filter_policy.selection
+            and bond_filter_key in self.filter_policy.selection
+        ):
+            self.filter_policy.deselect(bond_filter_key)
+
+        search_rewards = self.config.search.algorithm_config.get("search_rewards")
+        if not search_rewards:
+            return
+
+        if self.config.search.break_bonds and "broken bonds" in search_rewards:
+            if not target_mol.has_all_focussed_bonds(self.config.search.break_bonds):
+                raise ValueError("Bonds in 'break_bonds' must exist in target molecule")
+            self.scorers.load(BrokenBondsScorer(self.config))
+            self._num_objectives = len(search_rewards)
+
     def _setup_search_tree(self) -> None:
-        self._logger.debug("Defining tree root: %s" % self.target_smiles)
+        self._logger.debug(f"Defining tree root:  {self.target_smiles}")
         if self.config.search.algorithm.lower() == "mcts":
             self.tree = MctsSearchTree(
                 root_smiles=self.target_smiles, config=self.config
@@ -259,6 +291,50 @@ def _setup_search_tree(self) -> None:
             cls = load_dynamic_class(self.config.search.algorithm)
             self.tree = cls(root_smiles=self.target_smiles, config=self.config)
 
+    def _setup_analysis(
+        self,
+        scorer: Optional[Union[str, List[str]]],
+    ) -> TreeAnalysis:
+        """Configure TreeAnalysis
+
+        :param scorer: a reference to the object used to score the nodes, can be a list
+        :returns: the configured TreeAnalysis
+        :raises ValueError: if the search tree not initialized
+        """
+        if not self.tree:
+            raise ValueError("Search tree not initialized")
+
+        if scorer is None:
+            scorer_names = self.config.post_processing.route_scorers
+            # If not defined, use the same scorer as the search rewards
+            if not scorer_names:
+                search_rewards = self.config.search.algorithm_config.get(
+                    "search_rewards"
+                )
+                scorer_names = search_rewards if search_rewards else ["state score"]
+
+        elif isinstance(scorer, str):
+            scorer_names = [scorer]
+        else:
+            scorer_names = list(scorer)
+
+        if "broken bonds" in scorer_names:
+            # Add broken bonds scorer if required
+            self.scorers.load(BrokenBondsScorer(self.config))
+
+        scorers = [self.scorers[name] for name in scorer_names]
+
+        if self.config.post_processing.scorer_weights:
+            scorers = [
+                CombinedScorer(
+                    self.config,
+                    scorer_names,
+                    self.config.post_processing.scorer_weights,
+                )
+            ]
+
+        return TreeAnalysis(self.tree, scorers)
+
 
 class AiZynthExpander:
     """

aizynthfinder/analysis/routes.py

@@ -2,6 +2,7 @@
 """
 from __future__ import annotations
 
+import copy
 from typing import TYPE_CHECKING
 
 import numpy as np
@@ -12,12 +13,12 @@
 except ImportError:
     pass
 
-from aizynthfinder.analysis import TreeAnalysis
 from aizynthfinder.analysis.utils import CombinedReactionTrees, RouteSelectionArguments
 from aizynthfinder.reactiontree import SUPPORT_DISTANCES, ReactionTree
 from aizynthfinder.search.mcts import MctsNode, MctsSearchTree
 
 if TYPE_CHECKING:
+    from aizynthfinder.analysis import TreeAnalysis
     from aizynthfinder.context.scoring import Scorer
     from aizynthfinder.utils.type_utils import (
         Any,
@@ -26,6 +27,7 @@
         PilImage,
         Sequence,
         StrDict,
+        Union,
     )
 
 
@@ -64,11 +66,11 @@ def __init__(self, reaction_trees: Sequence[ReactionTree], **kwargs) -> None:
         self._update_route_dict(self.route_metadata, "route_metadata")
 
         self.nodes = self._unpack_kwarg_with_default("nodes", None, **kwargs)
-        self.scores = self._unpack_kwarg_with_default("scores", np.nan, **kwargs)
+        self.scores = self._unpack_kwarg_with_default("scores", dict, **kwargs)
         self.all_scores = self._unpack_kwarg_with_default("all_scores", dict, **kwargs)
 
         self._dicts: Optional[Sequence[StrDict]] = self._unpack_kwarg("dicts", **kwargs)
-        self._images: Optional[Sequence[PilImage]] = self._unpack_kwarg(
+        self._images: Optional[Sequence[Optional[PilImage]]] = self._unpack_kwarg(
             "images", **kwargs
         )
         self._jsons: Optional[Sequence[str]] = self._unpack_kwarg("jsons", **kwargs)
@@ -80,7 +82,9 @@ def __init__(self, reaction_trees: Sequence[ReactionTree], **kwargs) -> None:
 
     @classmethod
     def from_analysis(
-        cls, analysis: TreeAnalysis, selection: RouteSelectionArguments = None
+        cls,
+        analysis: TreeAnalysis,
+        selection: Optional[RouteSelectionArguments] = None,
     ) -> "RouteCollection":
         """
         Create a collection from a tree analysis.
@@ -90,8 +94,8 @@ def from_analysis(
         :return: the created collection
         """
         items, scores = analysis.sort(selection)
-        all_scores = [{repr(analysis.scorer): score} for score in scores]
-        kwargs = {"scores": scores, "all_scores": all_scores}
+        all_scores = copy.deepcopy(scores)
+        kwargs: Dict[str, Any] = {"scores": scores, "all_scores": all_scores}
         if isinstance(analysis.search_tree, MctsSearchTree):
             kwargs["nodes"] = items
             reaction_trees = [
@@ -120,10 +124,11 @@ def dicts(self) -> Sequence[StrDict]:
         return self._dicts
 
     @property
-    def images(self) -> Sequence[PilImage]:
+    def images(self) -> Sequence[Optional[PilImage]]:
         """Returns a list of pictoral representation of the routes"""
         if self._images is None:
             self._images = self.make_images()
+        assert self._images is not None
         return self._images
 
     @property