def test_sort_nodes_scorer(setup_analysis):
analysis, _ = setup_analysis(scorer=NumberOfReactionsScorer())
best_nodes, best_scores = analysis.sort_nodes()
assert len(best_nodes) == 10
assert best_scores[0] == 2
def test_scoring_branch_mcts_tree_in_stock(shared_datadir, default_config,
mock_stock):
mock_stock(
default_config,
"CC(C)(C)CO",
"CC(C)(C)OC(=O)N(CCCl)CCCl",
"N#CCc1cccc(O)c1F",
"O=[N+]([O-])c1ccccc1F",
"O=C1CCC(=O)N1Br",
"O=C=Nc1csc(C(F)(F)F)n1",
"CCC[Sn](Cl)(CCC)CCC",
"COc1ccc2ncsc2c1",
)
search_tree = SearchTree.from_json(
shared_datadir / "tree_with_branching.json", default_config)
nodes = list(search_tree.graph())
assert pytest.approx(StateScorer(default_config)(nodes[-1]),
abs=1e-3) == 0.950
assert NumberOfReactionsScorer()(nodes[-1]) == 14
assert NumberOfPrecursorsScorer(default_config)(nodes[-1]) == 8
assert NumberOfPrecursorsInStockScorer(default_config)(nodes[-1]) == 8
assert PriceSumScorer(default_config)(nodes[-1]) == 8
cost_score = RouteCostScorer(default_config)(nodes[-1])
assert pytest.approx(cost_score, abs=1e-3) == 77.4797
def test_number_of_reaction_scorer_node(shared_datadir, default_config):
search_tree = SearchTree.from_json(
shared_datadir / "tree_without_repetition.json", default_config)
nodes = list(search_tree.graph())
scorer = NumberOfReactionsScorer()
assert scorer(nodes[1]) == 1
def test_sort_routes(setup_analysis_andor_tree):
analysis = setup_analysis_andor_tree(scorer=NumberOfReactionsScorer())
best_routes, best_scores = analysis.sort()
assert len(best_routes) == 3
assert best_scores[0] == 1
def test_scoring_branched_route(load_reaction_tree, default_config):
tree = ReactionTree.from_dict(load_reaction_tree("branched_route.json"))
assert pytest.approx(StateScorer(default_config)(tree),
abs=1e-6) == 0.00012363
assert NumberOfReactionsScorer(default_config)(tree) == 14
assert NumberOfPrecursorsScorer(default_config)(tree) == 8
assert NumberOfPrecursorsInStockScorer(default_config)(tree) == 0
def test_scoring_branched_mcts_tree(shared_datadir, default_config):
search_tree = SearchTree.from_json(
shared_datadir / "tree_with_branching.json", default_config)
nodes = list(search_tree.graph())
assert pytest.approx(StateScorer()(nodes[-1]), abs=1e-6) == 0.00012363
assert NumberOfReactionsScorer()(nodes[-1]) == 14
assert NumberOfPrecursorsScorer(default_config)(nodes[-1]) == 8
assert NumberOfPrecursorsInStockScorer(default_config)(nodes[-1]) == 0
def test_rescore_collection_for_trees(default_config, setup_linear_reaction_tree):
rt = setup_linear_reaction_tree()
routes = RouteCollection(reaction_trees=[rt])
routes.compute_scores(StateScorer(default_config))
routes.rescore(NumberOfReactionsScorer())
assert routes.scores[0] == 2
assert np.round(routes.all_scores[0]["state score"], 3) == 0.994
assert routes.all_scores[0]["number of reactions"] == 2
def test_scoring_branched_route(default_config, setup_branched_reaction_tree):
tree = setup_branched_reaction_tree()
assert pytest.approx(StateScorer(default_config)(tree), abs=1e-4) == 0.9866
assert NumberOfReactionsScorer()(tree) == 4
assert NumberOfPrecursorsScorer(default_config)(tree) == 5
assert NumberOfPrecursorsInStockScorer(default_config)(tree) == 5
assert PriceSumScorer(default_config)(tree) == 5
cost_score = RouteCostScorer(default_config)(tree)
assert pytest.approx(cost_score, abs=1e-4) == 13.6563
def test_scorers_one_mcts_node(default_config):
tree = SearchTree(default_config, root_smiles="CCCCOc1ccc(CC(=O)N(C)O)cc1")
node = tree.root
assert pytest.approx(StateScorer(default_config)(node), abs=1e-3) == 0.0497
assert NumberOfReactionsScorer(default_config)(node) == 0
assert NumberOfPrecursorsScorer(default_config)(node) == 1
assert NumberOfPrecursorsInStockScorer(default_config)(node) == 0
assert PriceSumScorer(default_config)(node) == 10
assert RouteCostScorer(default_config)(node) == 10
def test_scoring_branched_route_not_in_stock(default_config,
setup_branched_reaction_tree):
tree = setup_branched_reaction_tree("O")
assert pytest.approx(StateScorer(default_config)(tree), abs=1e-4) == 0.7966
assert NumberOfReactionsScorer()(tree) == 4
assert NumberOfPrecursorsScorer(default_config)(tree) == 5
assert NumberOfPrecursorsInStockScorer(default_config)(tree) == 4
assert PriceSumScorer(default_config)(tree) == 14
cost_score = RouteCostScorer(default_config)(tree)
assert pytest.approx(cost_score, abs=1e-4) == 31.2344
def test_scorers_tree_one_node_route(default_config):
tree = ReactionTree()
tree.root = UniqueMolecule(smiles="CCCCOc1ccc(CC(=O)N(C)O)cc1")
tree.graph.add_node(tree.root)
assert pytest.approx(StateScorer(default_config)(tree), abs=1e-3) == 0.0497
assert NumberOfReactionsScorer(default_config)(tree) == 0
assert NumberOfPrecursorsScorer(default_config)(tree) == 1
assert NumberOfPrecursorsInStockScorer(default_config)(tree) == 0
assert PriceSumScorer(default_config)(tree) == 10
assert RouteCostScorer(default_config)(tree) == 10
def test_rescore_collection_for_trees(default_config, mock_stock,
load_reaction_tree):
mock_stock(default_config, "N#Cc1cccc(N)c1F", "O=C(Cl)c1ccc(F)cc1",
"CN1CCC(Cl)CC1", "O")
rt = ReactionTree.from_dict(load_reaction_tree("sample_reaction.json"))
routes = RouteCollection(reaction_trees=[rt])
routes.compute_scores(StateScorer(default_config))
routes.rescore(NumberOfReactionsScorer())
assert routes.scores[0] == 2
assert np.round(routes.all_scores[0]["state score"], 3) == 0.994
assert routes.all_scores[0]["number of reactions"] == 2
def test_rescore_collection(setup_analysis):
analysis, _ = setup_analysis()
routes = RouteCollection.from_analysis(analysis, 5)
routes.rescore(NumberOfReactionsScorer())
assert routes.scores[0] == 1
assert np.round(routes.all_scores[0]["state score"], 3) == 0.681
assert routes.all_scores[0]["number of reactions"] == 1
assert np.round(routes.all_scores[1]["state score"], 3) == 0.523
assert routes.scores[1] == 1
assert routes.all_scores[1]["number of reactions"] == 1
def test_compute_new_score(setup_analysis):
analysis, _ = setup_analysis()
routes = RouteCollection.from_analysis(analysis, 5)
routes.compute_scores(NumberOfReactionsScorer())
assert np.round(routes.scores[0], 3) == 0.994
assert np.round(routes.all_scores[0]["state score"], 3) == 0.994
assert routes.all_scores[0]["number of reactions"] == 2
assert np.round(routes.scores[1], 3) == 0.681
assert np.round(routes.all_scores[1]["state score"], 3) == 0.681
assert routes.all_scores[1]["number of reactions"] == 1
def test_compute_new_score_for_trees(default_config, setup_linear_reaction_tree):
rt = setup_linear_reaction_tree()
routes = RouteCollection(reaction_trees=[rt])
assert routes.nodes[0] is None
assert routes.scores[0] is np.nan
assert routes.all_scores[0] == {}
routes.compute_scores(StateScorer(default_config), NumberOfReactionsScorer())
assert routes.scores[0] is np.nan
assert np.round(routes.all_scores[0]["state score"], 3) == 0.994
assert routes.all_scores[0]["number of reactions"] == 2
def test_tree_statistics_andor_tree(setup_analysis_andor_tree):
analysis = setup_analysis_andor_tree(scorer=NumberOfReactionsScorer())
stats = analysis.tree_statistics()
assert stats["number_of_nodes"] == 9
assert stats["max_transforms"] == 2
assert stats["max_children"] == 2
assert stats["number_of_routes"] == 3
assert stats["number_of_solved_routes"] == 3
assert stats["top_score"] == 1
assert stats["is_solved"]
assert stats["number_of_steps"] == 1
assert stats["number_of_precursors"] == 2
assert stats["number_of_precursors_in_stock"] == 2
mol_str = "Cc1ccc2nc3ccccc3c(Cl)c2c1, Nc1ccc(NC(=S)Nc2ccccc2)cc1"
assert stats["precursors_in_stock"] == mol_str
assert stats["precursors_not_in_stock"] == ""
assert stats["policy_used_counts"] == {}
def test_tree_statistics(setup_analysis):
analysis, _ = setup_analysis(scorer=NumberOfReactionsScorer())
stats = analysis.tree_statistics()
assert stats["number_of_nodes"] == 61
assert stats["max_transforms"] == 7
assert stats["max_children"] == 10
assert stats["number_of_leafs"] == 10
assert stats["number_of_solved_leafs"] == 1
assert stats["top_score"] == 2
assert stats["is_solved"]
assert stats["number_of_steps"] == 2
assert stats["number_of_precursors"] == 4
assert stats["number_of_precursors_in_stock"] == 4
mol_str = "CN1CCC(Cl)CC1, O, N#Cc1cccc(N)c1F, O=C(Cl)c1ccc(F)cc1"
assert stats["precursors_in_stock"] == mol_str
assert stats["precursors_not_in_stock"] == ""
assert stats["policy_used_counts"] == {}
def test_scoring_branched_route_in_stock(load_reaction_tree, default_config,
mock_stock):
mock_stock(
default_config,
"CC(C)(C)CO",
"CC(C)(C)OC(=O)N(CCCl)CCCl",
"N#CCc1cccc(O)c1F",
"O=[N+]([O-])c1ccccc1F",
"O=C1CCC(=O)N1Br",
"O=C=Nc1csc(C(F)(F)F)n1",
"CCC[Sn](Cl)(CCC)CCC",
"COc1ccc2ncsc2c1",
)
tree = ReactionTree.from_dict(load_reaction_tree("branched_route.json"))
assert pytest.approx(StateScorer(default_config)(tree), abs=1e-3) == 0.950
assert NumberOfReactionsScorer(default_config)(tree) == 14
assert NumberOfPrecursorsScorer(default_config)(tree) == 8
assert NumberOfPrecursorsInStockScorer(default_config)(tree) == 8
assert PriceSumScorer(default_config)(tree) == 8
cost_score = RouteCostScorer(default_config)(tree)
assert pytest.approx(cost_score, abs=1e-3) == 77.4797
def test_number_of_reaction_scorer_tree(load_reaction_tree):
tree = ReactionTree.from_dict(load_reaction_tree("sample_reaction.json"))
scorer = NumberOfReactionsScorer()
assert scorer(tree) == 2
def test_best_route(setup_analysis_andor_tree):
analysis = setup_analysis_andor_tree(scorer=NumberOfReactionsScorer())
assert len(list(analysis.best().reactions())) == 1
def test_best_node(setup_analysis):
analysis, nodes = setup_analysis(scorer=NumberOfReactionsScorer())
assert analysis.best_node() is nodes[51]