Publishing version 2.0.0

CHANGELOG.md

@@ -1,5 +1,27 @@
 # CHANGELOG
 
+## Version 2.0.0 - 2020-11-24 (2020-09-29)
+
+### Breaking changes
+
+- The `Reaction` class is renamed to `RetroReaction`
+- The `Policy` class is renamed to `ExpansionPolicy`
+- `config`, `stock`, `policy` and `scoring` modules moved to `aizynthfinder.context` package
+- Tool "preprocess_rollout" is now called "preprocess_expansion"
+- Some of the public methods of `Stock`, `Policy` and `ScorerCollection` classes are renamed
+- Setting target molecule now destroys the search tree
+
+### Features
+
+- Add a filter policy to MCTS to remove unfeasible reactions
+- 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 
+- Introduce possibility to remove unsantizable reactions from template library when training
+- Catch exceptions from RDChiral more gracefully
+
+
 ## Version 1.2.0 - 2020-11-24 (2020-09-04)
 
 ### Features

README.md

@@ -10,7 +10,7 @@ Before you begin, ensure you have met the following requirements:
 
 The tool has been developed on a Linux platform, but the software has been tested on Windows 10 and macOS Catalina.
 
-* You have installed anaconda or miniconda with python 3.6 or 3.7
+* You have installed anaconda or miniconda with python 3.6, 3.7 or 3.8
 
 
 ## Installation
@@ -30,7 +30,7 @@ if you have GPU and CUDA libraries enabled on your machine, you can install the
 * Secondly, install the ``aizynthfinder`` package
 
 ```
-python -m pip install https://github.com/MolecularAI/aizynthfinder/archive/v1.0.0.tar.gz
+python -m pip install https://github.com/MolecularAI/aizynthfinder/archive/v2.0.0.tar.gz
 ```
 
 if you want to install the latest version
@@ -61,8 +61,9 @@ To use the tool you need
 
     1. A stock file
     2. A trained rollout policy network (including the Keras model and the list of unique templates)
+    3. A trained filer 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) or they can be downloaded automatically using
+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
 
 ```
 download_public_data my_folder
@@ -111,7 +112,7 @@ Please use ``black`` package for formatting, and follow ``pep8`` style guide.
 * [@EBjerrum](https://www.github.com/EBjerrum)
 * [@A-Thakkar](https://www.github.com/A-Thakkar)
 
-The contributors have limited time for support questions, but pleae do not hesitate to submit an issue (see above).
+The contributors have limited time for support questions, but please do not hesitate to submit an issue (see above).
 
 ## License
 
@@ -120,4 +121,4 @@ The software is licensed under the MIT license (see LICENSE file), and is free a
 ## References
 
 1. Thakkar A, Kogej T, Reymond J-L, et al (2019) Datasets and their influence on the development of computer assisted synthesis planning tools in the pharmaceutical domain. Chem Sci. https://doi.org/10.1039/C9SC04944D
-2. Genheden S, Thakkar A, Chadimova V, et al (2020) AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning. ChemRxiv. Preprint. https://doi.org/10.26434/chemrxiv.12465371.v1
+2. Genheden S, Thakkar A, Chadimova V, et al (2020) AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning. J. Cheminf. https://jcheminf.biomedcentral.com/articles/10.1186/s13321-020-00472-1

aizynthfinder/aizynthfinder.py

@@ -6,7 +6,7 @@
 
 # This must be imported first to setup logging for rdkit, tensorflow etc
 from aizynthfinder.utils.logging import logger
-from aizynthfinder.mcts.config import Configuration
+from aizynthfinder.context.config import Configuration
 from aizynthfinder.mcts.mcts import SearchTree
 from aizynthfinder.analysis import TreeAnalysis, RouteCollection
 from aizynthfinder.chem import Molecule
@@ -16,8 +16,8 @@ class AiZynthFinder:
     """
     Public API to the aizynthfinder tool
 
-    If intantiated with the path to a yaml file or dictionary of settings 
-    the stocks and policy networks are loaded directly. 
+    If intantiated with the path to a yaml file or dictionary of settings
+    the stocks and policy networks are loaded directly.
     Otherwise, the user is responsible for loading them prior to
     executing the tree search.
 
@@ -52,7 +52,8 @@ def __init__(self, configfile=None, configdict=None):
         else:
             self.config = Configuration()
 
-        self.policy = self.config.policy
+        self.expansion_policy = self.config.expansion_policy
+        self.filter_policy = self.config.filter_policy
         self.stock = self.config.stock
         self.scorers = self.config.scorers
         self.tree = None
@@ -87,6 +88,7 @@ def target_mol(self):
 
     @target_mol.setter
     def target_mol(self, mol):
+        self.tree = None
         self._target_mol = mol
 
     def build_routes(self, min_nodes=5, scorer="state score"):
@@ -135,8 +137,12 @@ def run_from_json(self, params):
         :return: dictionary with all settings and top scored routes
         :rtype: dict
         """
-        self.stock.select_stocks(params["stocks"])
-        self.policy.select_policies(params.get("policy", params.get("policies", "")))
+        self.stock.select(params["stocks"])
+        self.expansion_policy.select(params.get("policy", params.get("policies", "")))
+        if "filter" in params:
+            self.filter_policy.select(params["filter"])
+        else:
+            self.filter_policy.deselect()
         self.config.C = params["C"]
         self.config.max_transforms = params["max_transforms"]
         self.config.cutoff_cumulative = params["cutoff_cumulative"]
@@ -146,6 +152,7 @@ def run_from_json(self, params):
         self.config.time_limit = params["time_limit"]
         self.config.iteration_limit = params["iteration_limit"]
         self.config.exclude_target_from_stock = params["exclude_target_from_stock"]
+        self.config.filter_cutoff = params["filter_cutoff"]
 
         self.prepare_tree()
         self.tree_search()
@@ -213,15 +220,15 @@ def _get_settings(self):
         """Get the current settings as a dictionary
         """
         # To be backward-compatible
-        if len(self.policy.selected_policies) == 1:
-            policy_value = self.policy.selected_policies[0]
+        if len(self.expansion_policy.selection) == 1:
+            policy_value = self.expansion_policy.selection[0]
             policy_key = "policy"
         else:
-            policy_value = self.policy.selected_policies
+            policy_value = self.expansion_policy.selection
             policy_key = "policies"
 
-        return {
-            "stocks": self.stock.selected_stocks,
+        dict_ = {
+            "stocks": self.stock.selection,
             policy_key: policy_value,
             "C": self.config.C,
             "max_transforms": self.config.max_transforms,
@@ -232,4 +239,8 @@ def _get_settings(self):
             "time_limit": self.config.time_limit,
             "iteration_limit": self.config.iteration_limit,
             "exclude_target_from_stock": self.config.exclude_target_from_stock,
+            "filter_cutoff": self.config.filter_cutoff,
         }
+        if self.filter_policy.selection:
+            dict_["filter"] = self.filter_policy.selection
+        return dict_

aizynthfinder/analysis.py

@@ -31,7 +31,7 @@ def __init__(self, search_tree, scorer=None):
         self.search_tree = search_tree
         if scorer is None:
             # Do import here to avoid circular imports
-            from aizynthfinder.scoring import StateScorer
+            from aizynthfinder.context.scoring import StateScorer
 
             self.scorer = StateScorer()
         else: