def setUp(self):
# create a scaffold filter and fill it with a few entries
smiles = [
"O=S(=O)(c3ccc(n1nc(cc1c2ccc(cc2)C)C(F)(F)F)cc3)N", "CCC", "CCCCC"
]
scores = np.array([0.7, 0.5, 0.])
valid_idx = [0, 1, 2]
component_parameters = ComponentParameters(
component_type=self.sf_enum.TANIMOTO_SIMILARITY,
name="tanimoto_similarity",
weight=1.,
smiles=smiles,
model_path="",
specific_parameters={})
component_score_summary = ComponentSummary(scores,
component_parameters)
final_summary = FinalSummary(scores, smiles, valid_idx,
[component_score_summary],
[component_score_summary])
sf_parameters = ScaffoldParameters(
name=self.scaffold_enum.IDENTICAL_MURCKO_SCAFFOLD,
minscore=0.5,
minsimilarity=0.4,
nbmax=1)
scaffold_filter_factory = ScaffoldFilterFactory()
self.scaffold_filter = scaffold_filter_factory.load_scaffold_filter(
sf_parameters)
self.scaffold_filter.score(final_summary)
def calculate_score(self, molecules: List) -> ComponentSummary:
score, offtarget_score = self._calculate_offtarget_activity(
molecules, self._activity_params, self._off_target_params,
self._delta_params)
score_summary = ComponentSummary(total_score=score,
parameters=self._off_target_params)
return score_summary
def setUp(self):
self.scaffold_enum = ScaffoldFilterEnum()
self.sf_enum = ScoringFunctionComponentNameEnum()
self.workfolders = [MAIN_TEST_PATH]
# create a scaffold filter and fill it with a few entries
smiles = [
"O=S(=O)(c3ccc(n1nc(cc1c2ccc(cc2)C)C(F)(F)F)cc3)N", "CCC", "CCCCC",
"CC"
]
scores = np.array([1.0, 0.5, 0.4, 0.3])
valid_idx = [0, 1, 2, 3]
component_parameters = ComponentParameters(
component_type=self.sf_enum.TANIMOTO_SIMILARITY,
name="tanimoto_similarity",
weight=1.,
smiles=smiles,
model_path="",
specific_parameters={})
component_score_summary = ComponentSummary(scores,
component_parameters)
final_summary = FinalSummary(scores, smiles, valid_idx,
[component_score_summary],
[component_score_summary])
sf_parameters = ScaffoldParameters(name=self.scaffold_enum.NO_FILTER,
minscore=0.5,
minsimilarity=0.4,
nbmax=1)
scaffold_filter_factory = ScaffoldFilterFactory()
self.scaffold_filter = scaffold_filter_factory.load_scaffold_filter(
sf_parameters)
self.scaffold_filter.score(final_summary)
def _update_total_score(summary: ComponentSummary, query_length: int,
valid_indices: List[int]) -> ComponentSummary:
total_score = np.full(query_length, 0, dtype=np.float32)
assert len(valid_indices) == len(summary.total_score)
for idx, value in zip(valid_indices, summary.total_score):
total_score[idx] = value
summary.total_score = total_score
return summary
def test_invalid_addition(self):
# try to add a smile already present
smiles = ["CCC"]
scores = np.array([1.0])
valid_idx = [0]
component_parameters = ComponentParameters(
component_type=self.sf_enum.TANIMOTO_SIMILARITY,
name="tanimoto_similarity",
weight=1.,
smiles=smiles,
model_path="",
specific_parameters={})
component_score_summary = ComponentSummary(scores,
component_parameters)
final_summary = FinalSummary(scores, smiles, valid_idx,
[component_score_summary],
[component_score_summary])
self.scaffold_filter.score(final_summary)
self.assertEqual(2, len(self.scaffold_filter.scaffolds))
def calculate_score(self, molecules: List) -> ComponentSummary:
score = self._substructure_match(molecules, self.custom_alerts)
score_summary = ComponentSummary(total_score=score,
parameters=self.parameters)
return score_summary
def calculate_score(self, molecules: List) -> ComponentSummary:
score = self.activity_model.predict_from_mols(molecules, self.parameters.specific_parameters)
score_summary = ComponentSummary(total_score=score, parameters=self.parameters)
return score_summary
def calculate_score(self, molecules: List) -> ComponentSummary:
query_fps = self._mols_to_fingerprints(molecules)
score = self._calculate_tanimoto(query_fps, self._fingerprints)
score_summary = ComponentSummary(total_score=score,
parameters=self.parameters)
return score_summary
def calculate_score(self, molecules: List) -> ComponentSummary:
score = self._calculate_score(molecules)
score_summary = ComponentSummary(total_score=score,
parameters=self.parameters)
return score_summary