def __init__(self, parameters: List[ComponentParameters], parallel=False):
self.component_enum = ScoringFunctionComponentNameEnum()
self.component_specific_parameters = ComponentSpecificParametersEnum()
factory = ScoreComponentFactory(parameters)
self.scoring_components = factory.create_score_components()
if parallel:
self.get_final_score = self._parallel_final_score
def setUpClass(self):
csp_enum = ComponentSpecificParametersEnum()
transf_type = TransformationTypeEnum()
sf_enum = ScoringFunctionComponentNameEnum()
activity = create_activity_component_regression()
activity.specific_parameters[
csp_enum.TRANSFORMATION_TYPE] = transf_type.DOUBLE_SIGMOID
activity.specific_parameters[csp_enum.COEF_DIV] = 100.
activity.specific_parameters[csp_enum.COEF_SI] = 150.
activity.specific_parameters[csp_enum.COEF_SE] = 150.
off_activity = create_offtarget_activity_component_regression()
delta_params = {
"high": 3.0,
"k": 0.25,
"low": 0.0,
"transformation": True,
"transformation_type": "sigmoid"
}
selectivity = ComponentParameters(
component_type=sf_enum.SELECTIVITY,
name="desirability",
weight=1.,
smiles=[],
model_path="",
specific_parameters={
"activity_model_path": activity.model_path,
"offtarget_model_path": off_activity.model_path,
"activity_specific_parameters":
activity.specific_parameters.copy(),
"offtarget_specific_parameters":
off_activity.specific_parameters,
"delta_transformation_parameters": delta_params
})
qed_score = ComponentParameters(component_type=sf_enum.QED_SCORE,
name="qed_score",
weight=1.,
smiles=[],
model_path="",
specific_parameters={})
matching_substructure = ComponentParameters(
component_type=sf_enum.MATCHING_SUBSTRUCTURE,
name="matching_substructure",
weight=1.,
smiles=["[*]n1cccc1CC"],
model_path="",
specific_parameters={})
custom_alerts = create_custom_alerts_configuration()
self.sf_state = CustomProduct(parameters=[
activity, selectivity, qed_score, matching_substructure,
custom_alerts
])
def _specific_parameters_classifiaction_predictive_model():
csp_enum = ComponentSpecificParametersEnum()
descriptor_types = DescriptorTypesEnum()
specific_parameters = {csp_enum.HIGH: 9,
csp_enum.LOW: 4,
csp_enum.K: 0.25,
csp_enum.TRANSFORMATION: False,
csp_enum.SCIKIT: "classification",
csp_enum.DESCRIPTOR_TYPE: descriptor_types.ECFP_COUNTS}
return specific_parameters
def setUpClass(self):
sf_enum = ScoringFunctionComponentNameEnum()
csp_enum = ComponentSpecificParametersEnum()
ts_parameters = ComponentParameters(
component_type=sf_enum.MOLECULAR_WEIGHT,
name="MW",
weight=1.,
smiles=[],
model_path="",
specific_parameters={csp_enum.TRANSFORMATION: False})
self.sf_state = CustomSum(parameters=[ts_parameters])
def setUpClass(self):
sf_enum = ScoringFunctionComponentNameEnum()
csp_enum = ComponentSpecificParametersEnum()
ts_parameters = ComponentParameters(
component_type=sf_enum.NUM_HBD_LIPINSKI,
name="NumHBD_Lipinski",
weight=1.,
smiles=[],
model_path="",
specific_parameters={csp_enum.TRANSFORMATION: False})
self.sf_state = CustomSum(parameters=[ts_parameters])
def setUpClass(self):
sf_enum = ScoringFunctionComponentNameEnum()
csp_enum = ComponentSpecificParametersEnum()
ts_parameters = ComponentParameters(
component_type=sf_enum.NUM_ROTATABLE_BONDS,
name="NumRotatableBonds",
weight=1.,
smiles=[],
model_path="",
specific_parameters={csp_enum.TRANSFORMATION: False})
self.sf_state = CustomSum(parameters=[ts_parameters])
def _specific_parameters_regression_predictive_model():
csp_enum = ComponentSpecificParametersEnum()
transf_type = TransformationTypeEnum()
descriptor_types = DescriptorTypesEnum()
specific_parameters = {csp_enum.HIGH: 9,
csp_enum.LOW: 4,
csp_enum.K: 0.25,
csp_enum.TRANSFORMATION: True,
csp_enum.TRANSFORMATION_TYPE: transf_type.SIGMOID,
csp_enum.SCIKIT: "regression",
csp_enum.DESCRIPTOR_TYPE: descriptor_types.ECFP_COUNTS}
return specific_parameters
def create_offtarget_activity_component_classification():
csp_enum = ComponentSpecificParametersEnum()
sf_enum = ScoringFunctionComponentNameEnum()
specific_parameters = _specific_parameters_classifiaction_predictive_model()
specific_parameters[csp_enum.HIGH] = 3
specific_parameters[csp_enum.LOW] = 0
specific_parameters[csp_enum.TRANSFORMATION] = False
parameters = ComponentParameters(component_type=sf_enum.PREDICTIVE_PROPERTY,
name="predictive_property_classification",
weight=1.,
smiles=[],
model_path=ACTIVITY_CLASSIFICATION,
specific_parameters=specific_parameters)
return parameters
def create_offtarget_activity_component_regression():
csp_enum = ComponentSpecificParametersEnum()
sf_enum = ScoringFunctionComponentNameEnum()
specific_parameters = _specific_parameters_regression_predictive_model()
specific_parameters[csp_enum.HIGH] = 3
specific_parameters[csp_enum.LOW] = 0
specific_parameters[csp_enum.TRANSFORMATION] = False
parameters = ComponentParameters(component_type=sf_enum.PREDICTIVE_PROPERTY,
name="offtarget_activity",
weight=1.,
smiles=[],
model_path=OFFTARGET_ACTIVITY_REGRESSION,
specific_parameters=specific_parameters)
return parameters
def create_c_lab_component(somponent_type):
csp_enum = ComponentSpecificParametersEnum()
transf_type = TransformationTypeEnum()
specific_parameters = {csp_enum.CLAB_INPUT_FILE: f"{MAIN_TEST_PATH}/clab_input.json",
csp_enum.HIGH: 9,
csp_enum.LOW: 4,
csp_enum.K: 0.25,
csp_enum.TRANSFORMATION: True,
csp_enum.TRANSFORMATION_TYPE: transf_type.SIGMOID}
parameters = ComponentParameters(component_type=somponent_type,
name="c_lab",
weight=1.,
smiles=[],
model_path="",
specific_parameters=specific_parameters)
return parameters
def setUpClass(self):
sf_enum = ScoringFunctionComponentNameEnum()
csp_enum = ComponentSpecificParametersEnum()
tt_enum = TransformationTypeEnum()
specific_parameters = {
csp_enum.TRANSFORMATION: True,
csp_enum.LOW: 0,
csp_enum.HIGH: 1,
csp_enum.TRANSFORMATION_TYPE: tt_enum.STEP
}
ts_parameters = ComponentParameters(
component_type=sf_enum.NUM_HBD_LIPINSKI,
name="NumHBD_Lipinski",
weight=1.,
smiles=[],
model_path="",
specific_parameters=specific_parameters)
self.sf_state = CustomSum(parameters=[ts_parameters])
def setUp(self):
self.rm_enums = RunningModeEnum()
self.cs_enum = ComponentSpecificParametersEnum()
self.tf_enum = TransformationTypeEnum()
self.sfc_enum = ScoringFunctionComponentNameEnum()
self.lm_enum = LoggingModeEnum()
self.parameters = create_activity_component_regression()
log_conf = BaseLoggerConfiguration(
sender="http://10.59.162.10:8081",
recipient=self.lm_enum.LOCAL,
logging_path=f"{MAIN_TEST_PATH}/log",
job_name="model_validation_test",
job_id="1")
self.configuration_envelope = GeneralConfigurationEnvelope(
parameters=vars(self.parameters),
logging=vars(log_conf),
run_type=self.rm_enums.VALIDATION,
version="2.0")
def setUpClass(self):
sf_enum = ScoringFunctionComponentNameEnum()
csp_enum = ComponentSpecificParametersEnum()
tt_enum = TransformationTypeEnum()
specific_parameters = {
csp_enum.TRANSFORMATION: True,
csp_enum.LOW: 2,
csp_enum.HIGH: 5,
csp_enum.TRANSFORMATION_TYPE: tt_enum.STEP
}
ts_parameters = ComponentParameters(
component_type=sf_enum.NUM_ROTATABLE_BONDS,
name="NumRotatableBonds",
weight=1.,
smiles=[],
model_path="",
specific_parameters=specific_parameters)
self.sf_state = CustomSum(parameters=[ts_parameters])
def setUpClass(cls):
csp_enum = ComponentSpecificParametersEnum()
transf_type = TransformationTypeEnum()
enum = ScoringFunctionComponentNameEnum()
delta_params = {
"high": 3.0,
"k": 0.25,
"low": 0.0,
"transformation": True,
"transformation_type": "sigmoid"
}
activity = create_activity_component_classification()
activity.specific_parameters[
csp_enum.TRANSFORMATION_TYPE] = transf_type.DOUBLE_SIGMOID
activity.specific_parameters[csp_enum.COEF_DIV] = 100.
activity.specific_parameters[csp_enum.COEF_SI] = 150.
activity.specific_parameters[csp_enum.COEF_SE] = 150.
off_activity = create_offtarget_activity_component_classification()
selectivity = ComponentParameters(
component_type=enum.SELECTIVITY,
name="desirability",
weight=1.,
smiles=[],
model_path="",
specific_parameters={
"activity_model_path":
activity.model_path,
"offtarget_model_path":
off_activity.model_path,
"activity_specific_parameters":
activity.specific_parameters.copy(),
"offtarget_specific_parameters":
off_activity.specific_parameters.copy(),
"delta_transformation_parameters":
delta_params
})
cls.component = SelectivityComponent(parameters=selectivity)
def setUp(self):
csp_enum = ComponentSpecificParametersEnum()
ts_parameters = create_activity_component_regression()
ts_parameters.specific_parameters[csp_enum.TRANSFORMATION] = False
# unzip the model for loading
if not os.path.isdir(MAIN_TEST_PATH):
os.makedirs(MAIN_TEST_PATH)
tmp_model_path = os.path.join(
MAIN_TEST_PATH,
os.path.splitext(os.path.basename(SAS_MODEL_PATH))[0])
with gzip.open(SAS_MODEL_PATH, "rb") as f_in:
with open(tmp_model_path, "wb") as f_out:
shutil.copyfileobj(f_in, f_out)
ts_parameters.model_path = tmp_model_path
self.query_smiles = ['n1cccc2ccccc12']
self.query_mols = [
chem_smiles.to_mol(smile) for smile in self.query_smiles
]
self.component = SASComponent(ts_parameters)
def setUpClass(self):
sf_enum = ScoringFunctionComponentNameEnum()
csp_enum = ComponentSpecificParametersEnum()
tt_enum = TransformationTypeEnum()
specific_parameters = {
csp_enum.TRANSFORMATION: True,
csp_enum.LOW: 0,
csp_enum.HIGH: 135,
csp_enum.COEF_DIV: 100,
csp_enum.COEF_SI: 200,
csp_enum.COEF_SE: 200,
csp_enum.TRANSFORMATION_TYPE: tt_enum.DOUBLE_SIGMOID
}
ts_parameters = ComponentParameters(
component_type=sf_enum.TPSA,
name="TPSA",
weight=1.,
smiles=[],
model_path="",
specific_parameters=specific_parameters)
self.sf_state = CustomSum(parameters=[ts_parameters])
def setUpClass(cls):
cls.tt_enum = TransformationTypeEnum()
cls.csp_enum = ComponentSpecificParametersEnum()
cls.v_list = [12.086, 0.0015, 7.9, 123.264, 77.80, 4.0, 111.12]
def __init__(self):
self._csp_enum = ComponentSpecificParametersEnum()
self._transformation_function_registry = self._default_transformation_function_registry(
)
def __init__(self, parameters: ComponentParameters):
self.component_specific_parameters = ComponentSpecificParametersEnum()
self.parameters = parameters
def setUpClass(cls):
cls.csp_enum = ComponentSpecificParametersEnum()
activity = create_predictive_property_component_regression()
cls.component = PredictivePropertyComponent(activity)