gt4sd.properties.molecules.core module¶

Summary¶

Classes:

`ActivityAgainstTarget`	Calculate the activity of a molecule against a target molecule.
`ActivityAgainstTargetParameters`
`Askcos`	A property predictor that uses the ASKCOs API to calculate the synthesizability of a molecule.
`AskcosParameters`
`Bertz`	Calculate Bertz index of a molecule.
`ClinTox`	Model to predict environmental toxicity for the 12 endpoints in Tox21.
`ClinToxParameters`
`Docking`	A property predictor that computes the docking score against a user-defined target.
`DockingParameters`
`DockingTdc`	A property predictor that computes the docking score against a target provided via the TDC package (see: https://tdcommons.ai/functions/oracles/#docking-scores)
`DockingTdcParameters`
`Esol`	Estimate the water solubility of a molecule.
`IsScaffold`	Whether a molecule is identical to its Murcko Scaffold.
`Lipinski`	Calculate whether a molecule adheres to the Lipinski-rule-of-5.
`Logp`	Calculates the partition coefficient of a molecule.
`MCAParameters`
`MolecularWeight`	Calculate molecular weight of a molecule.
`MoleculeOne`	A property predictor that uses the MoleculeOne API to calculate the synthesizability of a molecule.
`MoleculeOneParameters`
`MolformerClassification`	Class for all Molformer classification algorithms.
`MolformerClassificationParameters`
`MolformerMultitaskClassification`	Class for all Molformer multitask classification algorithms.
`MolformerMultitaskClassificationParameters`
`MolformerParameters`
`MolformerRegression`	Class for all Molformer regression algorithms.
`MolformerRegressionParameters`
`NumberAromaticRings`	Calculate number of aromatic rings of a molecule.
`NumberAtoms`	Calculate number of atoms of a molecule.
`NumberHAcceptors`	Calculate number of H acceptors of a molecule.
`NumberHDonors`	Calculate number of H donors of a molecule.
`NumberHeterocycles`	The amount of heterocycles of a molecule.
`NumberLargeRings`	Calculate the amount of large rings (> 6 atoms) of a molecule.
`NumberRings`	Calculate number of rings of a molecule.
`NumberRotatableBonds`	Calculate number of rotatable bonds of a molecule.
`NumberStereocenters`	The amount of stereo centers of a molecule.
`OrganTox`	Model to predict toxicity for different organs.
`OrganToxParameters`
`Plogp`	Calculate the penalized logP of a molecule.
`Qed`	Calculate the quantitative estimate of drug-likeness (QED) of a molecule.
`S3ParametersMolecules`
`Sas`	Calculate the synthetic accessibility score (SAS) for a molecule.
`Scscore`	Calculate the synthetic complexity score (SCScore) of a molecule.
`ScscoreConfiguration`
`Sider`
`SiderParameters`
`SimilaritySeed`	Calculate the similarity of a molecule to a seed molecule.
`SimilaritySeedParameters`
`Tox21`	Model to predict environmental toxicity for the 12 endpoints in Tox21.
`Tox21Parameters`
`Tpsa`	Calculate the total polar surface area of a molecule.

Reference¶

class ScscoreConfiguration(**data)[source]¶

Bases: PropertyPredictorParameters

score_scale: int¶

fp_len: int¶

fp_rad: int¶

__dict__¶

__pydantic_fields_set__: set[str]¶: The names of fields explicitly set during instantiation.

__pydantic_extra__: dict[str, Any] | None¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_private__: dict[str, Any] | None¶: Values of private attributes set on the model instance.

__abstractmethods__ = frozenset({})¶

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'fp_len': <class 'int'>, 'fp_rad': <class 'int'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'score_scale': <class 'int'>}¶

__class_vars__: ClassVar[set[str]] = {}¶: The names of the class variables defined on the model.

__doc__ = None¶

__module__ = 'gt4sd.properties.molecules.core'¶

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.molecules.core.ScscoreConfiguration'>, 'config': {'title': 'ScscoreConfiguration'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.properties.molecules.core.ScscoreConfiguration'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.molecules.core.ScscoreConfiguration'>>]}, 'ref': 'gt4sd.properties.molecules.core.ScscoreConfiguration:94662814500624', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'fp_len': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 1024, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'fp_rad': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 2, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'score_scale': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 5, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'ScscoreConfiguration', 'type': 'model-fields'}, 'type': 'model'}¶: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False¶: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶: The name of the post-init method for the model, if defined.

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x00005618676e8710, ), serializer: Fields( GeneralFieldsSerializer { fields: { "fp_rad": SerField { key_py: Py( 0x00007f1dce91a9b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea9468110, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "score_scale": SerField { key_py: Py( 0x00007f1dce91a930, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea9468170, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "fp_len": SerField { key_py: Py( 0x00007f1dce91a970, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dce9daff0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "ScscoreConfiguration", }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="ScscoreConfiguration", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "score_scale", lookup_key: Simple { key: "score_scale", py_key: Py( 0x00007f1dcc3c5c70, ), path: LookupPath( [ S( "score_scale", Py( 0x00007f1dcc3c5c30, ), ), ], ), }, name_py: Py( 0x00007f1dce91a930, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea9468170, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "fp_len", lookup_key: Simple { key: "fp_len", py_key: Py( 0x00007f1dcc3c5bf0, ), path: LookupPath( [ S( "fp_len", Py( 0x00007f1dcc3c5bb0, ), ), ], ), }, name_py: Py( 0x00007f1dce91a970, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dce9daff0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "fp_rad", lookup_key: Simple { key: "fp_rad", py_key: Py( 0x00007f1dcc3c5cb0, ), path: LookupPath( [ S( "fp_rad", Py( 0x00007f1dcc3c5d30, ), ), ], ), }, name_py: Py( 0x00007f1dce91a9b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea9468110, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, ], model_name: "ScscoreConfiguration", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x00005618676e8710, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f1ea71db950, ), name: "ScscoreConfiguration", }, ), definitions=[], cache_strings=True)¶: The pydantic-core SchemaValidator used to validate instances of the model.

__signature__: ClassVar[Signature] = <Signature (*, score_scale: int = 5, fp_len: int = 1024, fp_rad: int = 2) -> None>¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

_abc_impl = <_abc._abc_data object>¶

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'fp_len': FieldInfo(annotation=int, required=False, default=1024), 'fp_rad': FieldInfo(annotation=int, required=False, default=2), 'score_scale': FieldInfo(annotation=int, required=False, default=5)}¶

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

class SimilaritySeedParameters(**data)[source]¶

Bases: PropertyPredictorParameters

smiles: str¶

fp_key: str¶

__dict__¶

__pydantic_fields_set__: set[str]¶: The names of fields explicitly set during instantiation.

__pydantic_extra__: dict[str, Any] | None¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_private__: dict[str, Any] | None¶: Values of private attributes set on the model instance.

__abstractmethods__ = frozenset({})¶

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'fp_key': <class 'str'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'smiles': <class 'str'>}¶

__class_vars__: ClassVar[set[str]] = {}¶: The names of the class variables defined on the model.

__doc__ = None¶

__module__ = 'gt4sd.properties.molecules.core'¶

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.molecules.core.SimilaritySeedParameters'>, 'config': {'title': 'SimilaritySeedParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.properties.molecules.core.SimilaritySeedParameters'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.molecules.core.SimilaritySeedParameters'>>]}, 'ref': 'gt4sd.properties.molecules.core.SimilaritySeedParameters:94662815265856', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'fp_key': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'ECFP4', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'smiles': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'SimilaritySeedParameters', 'type': 'model-fields'}, 'type': 'model'}¶: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False¶: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶: The name of the post-init method for the model, if defined.

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x00005618677a3440, ), serializer: Fields( GeneralFieldsSerializer { fields: { "fp_key": SerField { key_py: Py( 0x00007f1dce91aa30, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dcfaad8b0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "smiles": SerField { key_py: Py( 0x00007f1ea52cd030, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "SimilaritySeedParameters", }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="SimilaritySeedParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "smiles", lookup_key: Simple { key: "smiles", py_key: Py( 0x00007f1dcc3c70f0, ), path: LookupPath( [ S( "smiles", Py( 0x00007f1dcc3c70b0, ), ), ], ), }, name_py: Py( 0x00007f1ea52cd030, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "fp_key", lookup_key: Simple { key: "fp_key", py_key: Py( 0x00007f1dcc3c7070, ), path: LookupPath( [ S( "fp_key", Py( 0x00007f1dcc3c6ff0, ), ), ], ), }, name_py: Py( 0x00007f1dce91aa30, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dcfaad8b0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, ], model_name: "SimilaritySeedParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x00005618677a3440, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f1ea71db950, ), name: "SimilaritySeedParameters", }, ), definitions=[], cache_strings=True)¶: The pydantic-core SchemaValidator used to validate instances of the model.

__signature__: ClassVar[Signature] = <Signature (*, smiles: str, fp_key: str = 'ECFP4') -> None>¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

_abc_impl = <_abc._abc_data object>¶

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'fp_key': FieldInfo(annotation=str, required=False, default='ECFP4'), 'smiles': FieldInfo(annotation=str, required=True, examples=['c1ccccc1'])}¶

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

class ActivityAgainstTargetParameters(**data)[source]¶

Bases: PropertyPredictorParameters

target: str¶

__dict__¶

__pydantic_fields_set__: set[str]¶: The names of fields explicitly set during instantiation.

__pydantic_extra__: dict[str, Any] | None¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_private__: dict[str, Any] | None¶: Values of private attributes set on the model instance.

__abstractmethods__ = frozenset({})¶

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'target': <class 'str'>}¶

__class_vars__: ClassVar[set[str]] = {}¶: The names of the class variables defined on the model.

__doc__ = None¶

__module__ = 'gt4sd.properties.molecules.core'¶

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.molecules.core.ActivityAgainstTargetParameters'>, 'config': {'title': 'ActivityAgainstTargetParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.properties.molecules.core.ActivityAgainstTargetParameters'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.molecules.core.ActivityAgainstTargetParameters'>>]}, 'ref': 'gt4sd.properties.molecules.core.ActivityAgainstTargetParameters:94662814520592', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'target': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'ActivityAgainstTargetParameters', 'type': 'model-fields'}, 'type': 'model'}¶: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False¶: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶: The name of the post-init method for the model, if defined.

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x00005618676ed510, ), serializer: Fields( GeneralFieldsSerializer { fields: { "target": SerField { key_py: Py( 0x00007f1ea9503370, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "ActivityAgainstTargetParameters", }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="ActivityAgainstTargetParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "target", lookup_key: Simple { key: "target", py_key: Py( 0x00007f1dcc3c7cb0, ), path: LookupPath( [ S( "target", Py( 0x00007f1dcc3c7c30, ), ), ], ), }, name_py: Py( 0x00007f1ea9503370, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, ], model_name: "ActivityAgainstTargetParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x00005618676ed510, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f1ea71db950, ), name: "ActivityAgainstTargetParameters", }, ), definitions=[], cache_strings=True)¶: The pydantic-core SchemaValidator used to validate instances of the model.

__signature__: ClassVar[Signature] = <Signature (*, target: str) -> None>¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

_abc_impl = <_abc._abc_data object>¶

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'target': FieldInfo(annotation=str, required=True, description='name of the target.', examples=['drd2'])}¶

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

class AskcosParameters(**data)[source]¶

Bases: IpAdressParameters

class Output(value)[source]¶

Bases: str, Enum

An enumeration.

plausability: str = 'plausibility'¶

num_step: str = 'num_step'¶

synthesizability: str = 'synthesizability'¶

price: str = 'price'¶

_generate_next_value_(start, count, last_values)¶

Generate the next value when not given.

name: the name of the member start: the initial start value or None count: the number of existing members last_value: the last value assigned or None

__module__ = 'gt4sd.properties.molecules.core'¶

__annotations__ = {'num_step': <class 'str'>, 'plausability': <class 'str'>, 'price': <class 'str'>, 'synthesizability': <class 'str'>}¶

__dict__ = mappingproxy({'_generate_next_value_': <function Enum._generate_next_value_>, '__module__': 'gt4sd.properties.molecules.core', '__annotations__': {'plausability': <class 'str'>, 'num_step': <class 'str'>, 'synthesizability': <class 'str'>, 'price': <class 'str'>}, '__doc__': 'An enumeration.', '__dict__': <attribute '__dict__' of 'Output' objects>, '__weakref__': <attribute '__weakref__' of 'Output' objects>, '_member_names_': ['plausability', 'num_step', 'synthesizability', 'price'], '_member_map_': {'plausability': <Output.plausability: 'plausibility'>, 'num_step': <Output.num_step: 'num_step'>, 'synthesizability': <Output.synthesizability: 'synthesizability'>, 'price': <Output.price: 'price'>}, '_member_type_': <class 'str'>, '_value2member_map_': {'plausibility': <Output.plausability: 'plausibility'>, 'num_step': <Output.num_step: 'num_step'>, 'synthesizability': <Output.synthesizability: 'synthesizability'>, 'price': <Output.price: 'price'>}, 'plausability': <Output.plausability: 'plausibility'>, 'num_step': <Output.num_step: 'num_step'>, 'synthesizability': <Output.synthesizability: 'synthesizability'>, 'price': <Output.price: 'price'>, '__repr__': <function Enum.__repr__>, '__str__': <function Enum.__str__>, '__format__': <function Enum.__format__>, '__new__': <function Enum.__new__>})¶

__weakref__¶: list of weak references to the object (if defined)

_member_names_ = ['plausability', 'num_step', 'synthesizability', 'price']¶

_member_map_ = {'num_step': Output.num_step, 'plausability': Output.plausability, 'price': Output.price, 'synthesizability': Output.synthesizability}¶

_member_type_¶: alias of str

_value2member_map_ = {'num_step': Output.num_step, 'plausibility': Output.plausability, 'price': Output.price, 'synthesizability': Output.synthesizability}¶

__doc__ = 'An enumeration.'¶

output: Output¶

save_json: bool¶

file_name: str¶

num_trials: int¶

max_depth: int¶

max_branching: int¶

expansion_time: int¶

max_ppg: int¶

template_count: int¶

max_cum_prob: float¶

chemical_property_logic: str¶

max_chemprop_c: int¶

max_chemprop_n: int¶

max_chemprop_o: int¶

max_chemprop_h: int¶

chemical_popularity_logic: str¶

min_chempop_reactants: int¶

min_chempop_products: int¶

filter_threshold: float¶

return_first: str¶

model_config: ClassVar[ConfigDict] = {'use_enum_values': True}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

__dict__¶

__pydantic_fields_set__: set[str]¶: The names of fields explicitly set during instantiation.

__pydantic_extra__: dict[str, Any] | None¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_private__: dict[str, Any] | None¶: Values of private attributes set on the model instance.

__abstractmethods__ = frozenset({})¶

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'chemical_popularity_logic': <class 'str'>, 'chemical_property_logic': <class 'str'>, 'expansion_time': <class 'int'>, 'file_name': <class 'str'>, 'filter_threshold': <class 'float'>, 'host_ip': 'str', 'max_branching': <class 'int'>, 'max_chemprop_c': <class 'int'>, 'max_chemprop_h': <class 'int'>, 'max_chemprop_n': <class 'int'>, 'max_chemprop_o': <class 'int'>, 'max_cum_prob': <class 'float'>, 'max_depth': <class 'int'>, 'max_ppg': <class 'int'>, 'min_chempop_products': <class 'int'>, 'min_chempop_reactants': <class 'int'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'num_trials': <class 'int'>, 'output': <enum 'Output'>, 'return_first': <class 'str'>, 'save_json': <class 'bool'>, 'template_count': <class 'int'>}¶

__class_vars__: ClassVar[set[str]] = {}¶: The names of the class variables defined on the model.

__doc__ = None¶

__module__ = 'gt4sd.properties.molecules.core'¶

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.molecules.core.AskcosParameters'>, 'config': {'title': 'AskcosParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.properties.molecules.core.AskcosParameters'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.molecules.core.AskcosParameters'>>]}, 'ref': 'gt4sd.properties.molecules.core.AskcosParameters:94662815280384', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'chemical_popularity_logic': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'none', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'chemical_property_logic': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'none', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'expansion_time': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 60, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'file_name': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'tree_builder_result.json', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'filter_threshold': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 0.1, 'schema': {'type': 'float'}, 'type': 'default'}, 'type': 'model-field'}, 'host_ip': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'max_branching': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 25, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'max_chemprop_c': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'max_chemprop_h': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'max_chemprop_n': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'max_chemprop_o': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'max_cum_prob': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 0.999, 'schema': {'type': 'float'}, 'type': 'default'}, 'type': 'model-field'}, 'max_depth': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 9, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'max_ppg': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 100, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'min_chempop_products': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 5, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'min_chempop_reactants': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 5, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'num_trials': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 5, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'output': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': Output.plausability, 'schema': {'function': {'function': operator.attrgetter('value'), 'type': 'no-info'}, 'schema': {'cls': <enum 'Output'>, 'members': [<Output.plausability: 'plausibility'>, <Output.num_step: 'num_step'>, <Output.synthesizability: 'synthesizability'>, <Output.price: 'price'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.molecules.core.AskcosParameters.Output:94662814441184', 'sub_type': 'str', 'type': 'enum'}, 'serialization': {'type': 'str'}, 'type': 'function-after'}, 'type': 'default'}, 'type': 'model-field'}, 'return_first': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'true', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'save_json': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': False, 'schema': {'type': 'bool'}, 'type': 'default'}, 'type': 'model-field'}, 'template_count': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 1000, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'AskcosParameters', 'type': 'model-fields'}, 'type': 'model'}¶: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False¶: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶: The name of the post-init method for the model, if defined.

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x00005618677a6d00, ), serializer: Fields( GeneralFieldsSerializer { fields: { "filter_threshold": SerField { key_py: Py( 0x00007f1dce909a20, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dce9da010, ), ), serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), ), required: true, }, "expansion_time": SerField { key_py: Py( 0x00007f1dce91ac30, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea9468850, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "max_chemprop_h": SerField { key_py: Py( 0x00007f1dce91adb0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea94680d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "max_depth": SerField { key_py: Py( 0x00007f1ea51d83b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea94681f0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "min_chempop_reactants": SerField { key_py: Py( 0x00007f1dce909980, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea9468170, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "host_ip": SerField { key_py: Py( 0x00007f1ea72d54b0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "max_chemprop_c": SerField { key_py: Py( 0x00007f1dce91acf0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea94680d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "template_count": SerField { key_py: Py( 0x00007f1dcfa56470, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dce9da2b0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "max_cum_prob": SerField { key_py: Py( 0x00007f1dce91acb0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dce9d9d30, ), ), serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), ), required: true, }, "output": SerField { key_py: Py( 0x00007f1ea9128db0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dcdb19540, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "return_first": SerField { key_py: Py( 0x00007f1dce91adf0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea914c1f0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "chemical_property_logic": SerField { key_py: Py( 0x00007f1dce9098e0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea88d1fb0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "num_trials": SerField { key_py: Py( 0x00007f1dce91abb0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea9468170, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "max_ppg": SerField { key_py: Py( 0x00007f1dce91ac70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea9468d50, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "save_json": SerField { key_py: Py( 0x00007f1dce91ab70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x000056185a463580, ), ), serializer: Bool( BoolSerializer, ), }, ), ), required: true, }, "max_chemprop_n": SerField { key_py: Py( 0x00007f1dce91ad30, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea94680d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "max_branching": SerField { key_py: Py( 0x00007f1dce91abf0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea94683f0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "max_chemprop_o": SerField { key_py: Py( 0x00007f1dce91ad70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea94680d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "chemical_popularity_logic": SerField { key_py: Py( 0x00007f1dce909930, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea88d1fb0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "min_chempop_products": SerField { key_py: Py( 0x00007f1dce9099d0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea9468170, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "file_name": SerField { key_py: Py( 0x00007f1ea9548170, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dce909890, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 21, }, ), has_extra: false, root_model: false, name: "AskcosParameters", }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="AskcosParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "host_ip", lookup_key: Simple { key: "host_ip", py_key: Py( 0x00007f1dcaf64870, ), path: LookupPath( [ S( "host_ip", Py( 0x00007f1dcaf64830, ), ), ], ), }, name_py: Py( 0x00007f1ea72d54b0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "output", lookup_key: Simple { key: "output", py_key: Py( 0x00007f1dcaf647f0, ), path: LookupPath( [ S( "output", Py( 0x00007f1dcaf647b0, ), ), ], ), }, name_py: Py( 0x00007f1ea9128db0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dcdb19540, ), ), on_error: Raise, validator: FunctionAfter( FunctionAfterValidator { validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x00005618676d9ee0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "plausibility": 0, "synthesizability": 2, "num_step": 1, "price": 3, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f1dcdb19540, ), Py( 0x00007f1dcdb195b0, ), Py( 0x00007f1dcdb19620, ), Py( 0x00007f1dcdb19690, ), ], }, missing: None, expected_repr: "'plausibility', 'num_step', 'synthesizability' or 'price'", strict: false, class_repr: "AskcosParameters.Output", name: "str-enum[AskcosParameters.Output]", }, ), func: Py( 0x00007f1dcdb4ad70, ), config: Py( 0x00007f1dcaf64740, ), name: "function-after[operator.attrgetter('value')(), str-enum[AskcosParameters.Output]]", field_name: None, info_arg: false, }, ), validate_default: false, copy_default: false, name: "default[function-after[operator.attrgetter('value')(), str-enum[AskcosParameters.Output]]]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "save_json", lookup_key: Simple { key: "save_json", py_key: Py( 0x00007f1dcaf648f0, ), path: LookupPath( [ S( "save_json", Py( 0x00007f1dcaf648b0, ), ), ], ), }, name_py: Py( 0x00007f1dce91ab70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x000056185a463580, ), ), on_error: Raise, validator: Bool( BoolValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[bool]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "file_name", lookup_key: Simple { key: "file_name", py_key: Py( 0x00007f1dcaf64930, ), path: LookupPath( [ S( "file_name", Py( 0x00007f1dcaf64970, ), ), ], ), }, name_py: Py( 0x00007f1ea9548170, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dce909890, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "num_trials", lookup_key: Simple { key: "num_trials", py_key: Py( 0x00007f1dcaf649b0, ), path: LookupPath( [ S( "num_trials", Py( 0x00007f1dcaf649f0, ), ), ], ), }, name_py: Py( 0x00007f1dce91abb0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea9468170, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "max_depth", lookup_key: Simple { key: "max_depth", py_key: Py( 0x00007f1dcaf64a30, ), path: LookupPath( [ S( "max_depth", Py( 0x00007f1dcaf64a70, ), ), ], ), }, name_py: Py( 0x00007f1ea51d83b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea94681f0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "max_branching", lookup_key: Simple { key: "max_branching", py_key: Py( 0x00007f1dcaf64ab0, ), path: LookupPath( [ S( "max_branching", Py( 0x00007f1dcaf64b30, ), ), ], ), }, name_py: Py( 0x00007f1dce91abf0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea94683f0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "expansion_time", lookup_key: Simple { key: "expansion_time", py_key: Py( 0x00007f1dcaf64af0, ), path: LookupPath( [ S( "expansion_time", Py( 0x00007f1dcaf64bb0, ), ), ], ), }, name_py: Py( 0x00007f1dce91ac30, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea9468850, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "max_ppg", lookup_key: Simple { key: "max_ppg", py_key: Py( 0x00007f1dcaf64b70, ), path: LookupPath( [ S( "max_ppg", Py( 0x00007f1dcaf64bf0, ), ), ], ), }, name_py: Py( 0x00007f1dce91ac70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea9468d50, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "template_count", lookup_key: Simple { key: "template_count", py_key: Py( 0x00007f1dcaf64c30, ), path: LookupPath( [ S( "template_count", Py( 0x00007f1dcaf64c70, ), ), ], ), }, name_py: Py( 0x00007f1dcfa56470, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dce9da2b0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "max_cum_prob", lookup_key: Simple { key: "max_cum_prob", py_key: Py( 0x00007f1dcaf64cf0, ), path: LookupPath( [ S( "max_cum_prob", Py( 0x00007f1dcaf64cb0, ), ), ], ), }, name_py: Py( 0x00007f1dce91acb0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dce9d9d30, ), ), on_error: Raise, validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), validate_default: false, copy_default: false, name: "default[float]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "chemical_property_logic", lookup_key: Simple { key: "chemical_property_logic", py_key: Py( 0x00007f1dcaf270f0, ), path: LookupPath( [ S( "chemical_property_logic", Py( 0x00007f1dcaf27140, ), ), ], ), }, name_py: Py( 0x00007f1dce9098e0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea88d1fb0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "max_chemprop_c", lookup_key: Simple { key: "max_chemprop_c", py_key: Py( 0x00007f1dcaf64d30, ), path: LookupPath( [ S( "max_chemprop_c", Py( 0x00007f1dcaf64d70, ), ), ], ), }, name_py: Py( 0x00007f1dce91acf0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea94680d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "max_chemprop_n", lookup_key: Simple { key: "max_chemprop_n", py_key: Py( 0x00007f1dcaf64db0, ), path: LookupPath( [ S( "max_chemprop_n", Py( 0x00007f1dcaf64df0, ), ), ], ), }, name_py: Py( 0x00007f1dce91ad30, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea94680d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "max_chemprop_o", lookup_key: Simple { key: "max_chemprop_o", py_key: Py( 0x00007f1dcaf64e30, ), path: LookupPath( [ S( "max_chemprop_o", Py( 0x00007f1dcaf64e70, ), ), ], ), }, name_py: Py( 0x00007f1dce91ad70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea94680d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "max_chemprop_h", lookup_key: Simple { key: "max_chemprop_h", py_key: Py( 0x00007f1dcaf64eb0, ), path: LookupPath( [ S( "max_chemprop_h", Py( 0x00007f1dcaf64ef0, ), ), ], ), }, name_py: Py( 0x00007f1dce91adb0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea94680d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "chemical_popularity_logic", lookup_key: Simple { key: "chemical_popularity_logic", py_key: Py( 0x00007f1dcaf270a0, ), path: LookupPath( [ S( "chemical_popularity_logic", Py( 0x00007f1dcaf27190, ), ), ], ), }, name_py: Py( 0x00007f1dce909930, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea88d1fb0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "min_chempop_reactants", lookup_key: Simple { key: "min_chempop_reactants", py_key: Py( 0x00007f1dcaf271e0, ), path: LookupPath( [ S( "min_chempop_reactants", Py( 0x00007f1dcaf27230, ), ), ], ), }, name_py: Py( 0x00007f1dce909980, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea9468170, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "min_chempop_products", lookup_key: Simple { key: "min_chempop_products", py_key: Py( 0x00007f1dcaf27280, ), path: LookupPath( [ S( "min_chempop_products", Py( 0x00007f1dcaf272d0, ), ), ], ), }, name_py: Py( 0x00007f1dce9099d0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea9468170, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "filter_threshold", lookup_key: Simple { key: "filter_threshold", py_key: Py( 0x00007f1dcaf27320, ), path: LookupPath( [ S( "filter_threshold", Py( 0x00007f1dcaf27370, ), ), ], ), }, name_py: Py( 0x00007f1dce909a20, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dce9da010, ), ), on_error: Raise, validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), validate_default: false, copy_default: false, name: "default[float]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "return_first", lookup_key: Simple { key: "return_first", py_key: Py( 0x00007f1dcaf64f30, ), path: LookupPath( [ S( "return_first", Py( 0x00007f1dcaf64f70, ), ), ], ), }, name_py: Py( 0x00007f1dce91adf0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea914c1f0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, ], model_name: "AskcosParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x00005618677a6d00, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f1ea71db950, ), name: "AskcosParameters", }, ), definitions=[], cache_strings=True)¶: The pydantic-core SchemaValidator used to validate instances of the model.

__signature__: ClassVar[Signature] = <Signature (*, host_ip: str, output: gt4sd.properties.molecules.core.AskcosParameters.Output = <Output.plausability: 'plausibility'>, save_json: bool = False, file_name: str = 'tree_builder_result.json', num_trials: int = 5, max_depth: int = 9, max_branching: int = 25, expansion_time: int = 60, max_ppg: int = 100, template_count: int = 1000, max_cum_prob: float = 0.999, chemical_property_logic: str = 'none', max_chemprop_c: int = 0, max_chemprop_n: int = 0, max_chemprop_o: int = 0, max_chemprop_h: int = 0, chemical_popularity_logic: str = 'none', min_chempop_reactants: int = 5, min_chempop_products: int = 5, filter_threshold: float = 0.1, return_first: str = 'true') -> None>¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

_abc_impl = <_abc._abc_data object>¶

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_fields: ClassVar[Dict[str, FieldInfo]] = {'chemical_popularity_logic': FieldInfo(annotation=str, required=False, default='none'), 'chemical_property_logic': FieldInfo(annotation=str, required=False, default='none'), 'expansion_time': FieldInfo(annotation=int, required=False, default=60), 'file_name': FieldInfo(annotation=str, required=False, default='tree_builder_result.json'), 'filter_threshold': FieldInfo(annotation=float, required=False, default=0.1), 'host_ip': FieldInfo(annotation=str, required=True, description='The host IP address to access the service', examples=['xx.xx.xxx.xxx']), 'max_branching': FieldInfo(annotation=int, required=False, default=25), 'max_chemprop_c': FieldInfo(annotation=int, required=False, default=0), 'max_chemprop_h': FieldInfo(annotation=int, required=False, default=0), 'max_chemprop_n': FieldInfo(annotation=int, required=False, default=0), 'max_chemprop_o': FieldInfo(annotation=int, required=False, default=0), 'max_cum_prob': FieldInfo(annotation=float, required=False, default=0.999), 'max_depth': FieldInfo(annotation=int, required=False, default=9), 'max_ppg': FieldInfo(annotation=int, required=False, default=100), 'min_chempop_products': FieldInfo(annotation=int, required=False, default=5), 'min_chempop_reactants': FieldInfo(annotation=int, required=False, default=5), 'num_trials': FieldInfo(annotation=int, required=False, default=5), 'output': FieldInfo(annotation=AskcosParameters.Output, required=False, default=<Output.plausability: 'plausibility'>, description='Main output return type from ASKCOS', examples=[<Output.synthesizability: 'synthesizability'>]), 'return_first': FieldInfo(annotation=str, required=False, default='true'), 'save_json': FieldInfo(annotation=bool, required=False, default=False), 'template_count': FieldInfo(annotation=int, required=False, default=1000)}¶

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

class MoleculeOneParameters(**data)[source]¶

Bases: ApiTokenParameters

oracle_name: str¶

__dict__¶

__pydantic_fields_set__: set[str]¶: The names of fields explicitly set during instantiation.

__pydantic_extra__: dict[str, Any] | None¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_private__: dict[str, Any] | None¶: Values of private attributes set on the model instance.

__abstractmethods__ = frozenset({})¶

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'api_token': 'str', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'oracle_name': <class 'str'>}¶

__class_vars__: ClassVar[set[str]] = {}¶: The names of the class variables defined on the model.

__doc__ = None¶

__module__ = 'gt4sd.properties.molecules.core'¶

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.molecules.core.MoleculeOneParameters'>, 'config': {'title': 'MoleculeOneParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.properties.molecules.core.MoleculeOneParameters'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.molecules.core.MoleculeOneParameters'>>]}, 'ref': 'gt4sd.properties.molecules.core.MoleculeOneParameters:94662813599104', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'api_token': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'oracle_name': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'Molecule One Synthesis', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'MoleculeOneParameters', 'type': 'model-fields'}, 'type': 'model'}¶: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False¶: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶: The name of the post-init method for the model, if defined.

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000056186760c580, ), serializer: Fields( GeneralFieldsSerializer { fields: { "oracle_name": SerField { key_py: Py( 0x00007f1dce91ae70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dce909a70, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "api_token": SerField { key_py: Py( 0x00007f1de214f970, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "MoleculeOneParameters", }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="MoleculeOneParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "api_token", lookup_key: Simple { key: "api_token", py_key: Py( 0x00007f1dcdb569f0, ), path: LookupPath( [ S( "api_token", Py( 0x00007f1dcdb56b70, ), ), ], ), }, name_py: Py( 0x00007f1de214f970, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "oracle_name", lookup_key: Simple { key: "oracle_name", py_key: Py( 0x00007f1dcdb54630, ), path: LookupPath( [ S( "oracle_name", Py( 0x00007f1dcdb56a70, ), ), ], ), }, name_py: Py( 0x00007f1dce91ae70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dce909a70, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, ], model_name: "MoleculeOneParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000056186760c580, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f1ea71db950, ), name: "MoleculeOneParameters", }, ), definitions=[], cache_strings=True)¶: The pydantic-core SchemaValidator used to validate instances of the model.

__signature__: ClassVar[Signature] = <Signature (*, api_token: str, oracle_name: str = 'Molecule One Synthesis') -> None>¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

_abc_impl = <_abc._abc_data object>¶

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'api_token': FieldInfo(annotation=str, required=True, description='The API token/key to access the service', examples=['apk-c9db......']), 'oracle_name': FieldInfo(annotation=str, required=False, default='Molecule One Synthesis')}¶

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

class DockingTdcParameters(**data)[source]¶

Bases: PropertyPredictorParameters

target: str¶

__dict__¶

__pydantic_fields_set__: set[str]¶: The names of fields explicitly set during instantiation.

__pydantic_extra__: dict[str, Any] | None¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_private__: dict[str, Any] | None¶: Values of private attributes set on the model instance.

__abstractmethods__ = frozenset({})¶

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'target': <class 'str'>}¶

__class_vars__: ClassVar[set[str]] = {}¶: The names of the class variables defined on the model.

__doc__ = None¶

__module__ = 'gt4sd.properties.molecules.core'¶

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.molecules.core.DockingTdcParameters'>, 'config': {'title': 'DockingTdcParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.properties.molecules.core.DockingTdcParameters'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.molecules.core.DockingTdcParameters'>>]}, 'ref': 'gt4sd.properties.molecules.core.DockingTdcParameters:94662812251088', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'target': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'DockingTdcParameters', 'type': 'model-fields'}, 'type': 'model'}¶: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False¶: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶: The name of the post-init method for the model, if defined.

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x00005618674c33d0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "target": SerField { key_py: Py( 0x00007f1ea9503370, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "DockingTdcParameters", }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="DockingTdcParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "target", lookup_key: Simple { key: "target", py_key: Py( 0x00007f1dcaf65930, ), path: LookupPath( [ S( "target", Py( 0x00007f1dcaf658b0, ), ), ], ), }, name_py: Py( 0x00007f1ea9503370, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, ], model_name: "DockingTdcParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x00005618674c33d0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f1ea71db950, ), name: "DockingTdcParameters", }, ), definitions=[], cache_strings=True)¶: The pydantic-core SchemaValidator used to validate instances of the model.

__signature__: ClassVar[Signature] = <Signature (*, target: str) -> None>¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

_abc_impl = <_abc._abc_data object>¶

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'target': FieldInfo(annotation=str, required=True, description='Target for docking, provided via TDC', examples=['1iep_docking', '2rgp_docking', '3eml_docking', '3ny8_docking', '4rlu_docking', '4unn_docking', '5mo4_docking', '7l11_docking', 'drd3_docking', '3pbl_docking', '1iep_docking_normalize', '2rgp_docking_normalize', '3eml_docking_normalize', '3ny8_docking_normalize', '4rlu_docking_normalize', '4unn_docking_normalize', '5mo4_docking_normalize', '7l11_docking_normalize', 'drd3_docking_normalize', '3pbl_docking_normalize', '1iep_docking_vina', '2rgp_docking_vina', '3eml_docking_vina', '3ny8_docking_vina', '4rlu_docking_vina', '4unn_docking_vina', '5mo4_docking_vina', '7l11_docking_vina', 'drd3_docking_vina', '3pbl_docking_vina'])}¶

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

class DockingParameters(**data)[source]¶

Bases: PropertyPredictorParameters

name: str¶

receptor_pdb_file: str¶

box_center: List[int]¶

box_size: List[float]¶

__dict__¶

__pydantic_fields_set__: set[str]¶: The names of fields explicitly set during instantiation.

__pydantic_extra__: dict[str, Any] | None¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_private__: dict[str, Any] | None¶: Values of private attributes set on the model instance.

__abstractmethods__ = frozenset({})¶

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'box_center': typing.List[int], 'box_size': typing.List[float], 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'name': <class 'str'>, 'receptor_pdb_file': <class 'str'>}¶

__class_vars__: ClassVar[set[str]] = {}¶: The names of the class variables defined on the model.

__doc__ = None¶

__module__ = 'gt4sd.properties.molecules.core'¶

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.molecules.core.DockingParameters'>, 'config': {'title': 'DockingParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.properties.molecules.core.DockingParameters'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.molecules.core.DockingParameters'>>]}, 'ref': 'gt4sd.properties.molecules.core.DockingParameters:94662813691168', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'box_center': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'int'}, 'type': 'list'}, 'type': 'model-field'}, 'box_size': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'float'}, 'type': 'list'}, 'type': 'model-field'}, 'name': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'pyscreener', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'receptor_pdb_file': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'DockingParameters', 'type': 'model-fields'}, 'type': 'model'}¶: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False¶: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶: The name of the post-init method for the model, if defined.

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x0000561867622d20, ), serializer: Fields( GeneralFieldsSerializer { fields: { "name": SerField { key_py: Py( 0x00007f1ea94bcaf0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dce91aeb0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "receptor_pdb_file": SerField { key_py: Py( 0x00007f1dce909b60, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "box_center": SerField { key_py: Py( 0x00007f1dde2113f0, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Int( IntSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[int]", }, ), ), required: true, }, "box_size": SerField { key_py: Py( 0x00007f1dce91af30, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[float]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "DockingParameters", }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="DockingParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "name", lookup_key: Simple { key: "name", py_key: Py( 0x00007f1dcaf66a70, ), path: LookupPath( [ S( "name", Py( 0x00007f1dcaf66ab0, ), ), ], ), }, name_py: Py( 0x00007f1ea94bcaf0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dce91aeb0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "receptor_pdb_file", lookup_key: Simple { key: "receptor_pdb_file", py_key: Py( 0x00007f1dcaf27a50, ), path: LookupPath( [ S( "receptor_pdb_file", Py( 0x00007f1dcaf27aa0, ), ), ], ), }, name_py: Py( 0x00007f1dce909b60, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "box_center", lookup_key: Simple { key: "box_center", py_key: Py( 0x00007f1dcaf66a30, ), path: LookupPath( [ S( "box_center", Py( 0x00007f1dcaf669f0, ), ), ], ), }, name_py: Py( 0x00007f1dde2113f0, ), validator: List( ListValidator { strict: false, item_validator: Some( Int( IntValidator { strict: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, Field { name: "box_size", lookup_key: Simple { key: "box_size", py_key: Py( 0x00007f1dcaf66af0, ), path: LookupPath( [ S( "box_size", Py( 0x00007f1dcaf66b30, ), ), ], ), }, name_py: Py( 0x00007f1dce91af30, ), validator: List( ListValidator { strict: false, item_validator: Some( Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "DockingParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x0000561867622d20, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f1ea71db950, ), name: "DockingParameters", }, ), definitions=[], cache_strings=True)¶: The pydantic-core SchemaValidator used to validate instances of the model.

__signature__: ClassVar[Signature] = <Signature (*, name: str = 'pyscreener', receptor_pdb_file: str, box_center: List[int], box_size: List[float]) -> None>¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

_abc_impl = <_abc._abc_data object>¶

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'box_center': FieldInfo(annotation=List[int], required=True, description='Docking box center', examples=[[15.19, 53.903, 16.917]]), 'box_size': FieldInfo(annotation=List[float], required=True, description='Docking box size', examples=[[20, 20, 20]]), 'name': FieldInfo(annotation=str, required=False, default='pyscreener'), 'receptor_pdb_file': FieldInfo(annotation=str, required=True, description='Path to receptor PDB file', examples=['/tmp/2hbs.pdb'])}¶

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

class S3ParametersMolecules(**data)[source]¶

Bases: S3Parameters

domain: DomainSubmodule¶

__dict__¶

__pydantic_fields_set__: set[str]¶: The names of fields explicitly set during instantiation.

__pydantic_extra__: dict[str, Any] | None¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_private__: dict[str, Any] | None¶: Values of private attributes set on the model instance.

__abstractmethods__ = frozenset({})¶

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': 'str', 'algorithm_name': 'str', 'algorithm_type': 'str', 'algorithm_version': 'str', 'domain': <enum 'DomainSubmodule'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]'}¶

__class_vars__: ClassVar[set[str]] = {}¶: The names of the class variables defined on the model.

__doc__ = None¶

__module__ = 'gt4sd.properties.molecules.core'¶

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.molecules.core.S3ParametersMolecules'>, 'config': {'title': 'S3ParametersMolecules'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.properties.molecules.core.S3ParametersMolecules'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.molecules.core.S3ParametersMolecules'>>]}, 'ref': 'gt4sd.properties.molecules.core.S3ParametersMolecules:94662814955072', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'domain': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': DomainSubmodule.molecules, 'schema': {'cls': <enum 'DomainSubmodule'>, 'members': [<DomainSubmodule.molecules: 'molecules'>, <DomainSubmodule.properties: 'properties'>, <DomainSubmodule.crystals: 'crystals'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.core.DomainSubmodule:94662808869760', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'S3ParametersMolecules', 'type': 'model-fields'}, 'type': 'model'}¶: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False¶: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶: The name of the post-init method for the model, if defined.

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x0000561867757640, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_version": SerField { key_py: Py( 0x00007f1ea52ed250, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "algorithm_application": SerField { key_py: Py( 0x00007f1dcf4e0530, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "algorithm_type": SerField { key_py: Py( 0x00007f1ea52cf4f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dea152330, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_name": SerField { key_py: Py( 0x00007f1ea52ce770, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "domain": SerField { key_py: Py( 0x00007f1ea8dd1270, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dcea99230, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000561867189b80, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 5, }, ), has_extra: false, root_model: false, name: "S3ParametersMolecules", }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="S3ParametersMolecules", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_key: Simple { key: "algorithm_type", py_key: Py( 0x00007f1dcaddf130, ), path: LookupPath( [ S( "algorithm_type", Py( 0x00007f1dcadde1b0, ), ), ], ), }, name_py: Py( 0x00007f1ea52cf4f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dea152330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007f1dcaddc0b0, ), path: LookupPath( [ S( "domain", Py( 0x00007f1dcaddc270, ), ), ], ), }, name_py: Py( 0x00007f1ea8dd1270, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dcea99230, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000561867189b80, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "molecules": 0, "crystals": 2, "properties": 1, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f1dcea99230, ), Py( 0x00007f1dcea992a0, ), Py( 0x00007f1dcea99310, ), ], }, missing: None, expected_repr: "'molecules', 'properties' or 'crystals'", strict: false, class_repr: "DomainSubmodule", name: "str-enum[DomainSubmodule]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[DomainSubmodule]]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key: Simple { key: "algorithm_name", py_key: Py( 0x00007f1dcadde1f0, ), path: LookupPath( [ S( "algorithm_name", Py( 0x00007f1dcadde3b0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ce770, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key: Simple { key: "algorithm_version", py_key: Py( 0x00007f1dcaf84d50, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f1dcaf84df0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ed250, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_key: Simple { key: "algorithm_application", py_key: Py( 0x00007f1dcaf84da0, ), path: LookupPath( [ S( "algorithm_application", Py( 0x00007f1dcaf84e40, ), ), ], ), }, name_py: Py( 0x00007f1dcf4e0530, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, ], model_name: "S3ParametersMolecules", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x0000561867757640, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f1ea71db950, ), name: "S3ParametersMolecules", }, ), definitions=[], cache_strings=True)¶: The pydantic-core SchemaValidator used to validate instances of the model.

__signature__: ClassVar[Signature] = <Signature (*, algorithm_type: str = 'prediction', domain: gt4sd.properties.core.DomainSubmodule = <DomainSubmodule.molecules: 'molecules'>, algorithm_name: str, algorithm_version: str, algorithm_application: str) -> None>¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

_abc_impl = <_abc._abc_data object>¶

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'algorithm_application': FieldInfo(annotation=str, required=True, examples=['Tox21']), 'algorithm_name': FieldInfo(annotation=str, required=True, description='Name of the algorithm', examples=['MCA']), 'algorithm_type': FieldInfo(annotation=str, required=False, default='prediction'), 'algorithm_version': FieldInfo(annotation=str, required=True, description='Version of the algorithm', examples=['v0']), 'domain': FieldInfo(annotation=DomainSubmodule, required=False, default=<DomainSubmodule.molecules: 'molecules'>)}¶

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

class MolformerParameters(**data)[source]¶

Bases: S3ParametersMolecules

algorithm_name: str¶

batch_size: int¶

workers: int¶

device: Optional[str]¶

__dict__¶

__pydantic_fields_set__: set[str]¶: The names of fields explicitly set during instantiation.

__pydantic_extra__: dict[str, Any] | None¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_private__: dict[str, Any] | None¶: Values of private attributes set on the model instance.

__abstractmethods__ = frozenset({})¶

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': 'str', 'algorithm_name': <class 'str'>, 'algorithm_type': 'str', 'algorithm_version': 'str', 'batch_size': <class 'int'>, 'device': typing.Optional[str], 'domain': 'DomainSubmodule', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'workers': <class 'int'>}¶

__class_vars__: ClassVar[set[str]] = {}¶: The names of the class variables defined on the model.

__doc__ = None¶

__module__ = 'gt4sd.properties.molecules.core'¶

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.molecules.core.MolformerParameters'>, 'config': {'title': 'MolformerParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.properties.molecules.core.MolformerParameters'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.molecules.core.MolformerParameters'>>]}, 'ref': 'gt4sd.properties.molecules.core.MolformerParameters:94662813610176', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'molformer', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'batch_size': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 128, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'device': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'str'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'domain': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': DomainSubmodule.molecules, 'schema': {'cls': <enum 'DomainSubmodule'>, 'members': [<DomainSubmodule.molecules: 'molecules'>, <DomainSubmodule.properties: 'properties'>, <DomainSubmodule.crystals: 'crystals'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.core.DomainSubmodule:94662808869760', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'workers': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 8, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'MolformerParameters', 'type': 'model-fields'}, 'type': 'model'}¶: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False¶: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶: The name of the post-init method for the model, if defined.

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000056186760f0c0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_type": SerField { key_py: Py( 0x00007f1ea52cf4f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dea152330, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_version": SerField { key_py: Py( 0x00007f1ea52ed250, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "algorithm_application": SerField { key_py: Py( 0x00007f1dcf4e0530, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "batch_size": SerField { key_py: Py( 0x00007f1ea52ccc70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea94690d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "domain": SerField { key_py: Py( 0x00007f1ea8dd1270, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dcea99230, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000561867189b80, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, }, "workers": SerField { key_py: Py( 0x00007f1ea6a99070, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea94681d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "device": SerField { key_py: Py( 0x00007f1ea7046af0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x000056185a457740, ), ), serializer: Nullable( NullableSerializer { serializer: Str( StrSerializer, ), }, ), }, ), ), required: true, }, "algorithm_name": SerField { key_py: Py( 0x00007f1ea52ce770, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1e866fd2b0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 8, }, ), has_extra: false, root_model: false, name: "MolformerParameters", }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="MolformerParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_key: Simple { key: "algorithm_type", py_key: Py( 0x00007f1dcaf8cab0, ), path: LookupPath( [ S( "algorithm_type", Py( 0x00007f1dcaf8caf0, ), ), ], ), }, name_py: Py( 0x00007f1ea52cf4f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dea152330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007f1dcaf8ca70, ), path: LookupPath( [ S( "domain", Py( 0x00007f1dcaf8ca30, ), ), ], ), }, name_py: Py( 0x00007f1ea8dd1270, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dcea99230, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000561867189b80, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "molecules": 0, "crystals": 2, "properties": 1, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f1dcea99230, ), Py( 0x00007f1dcea992a0, ), Py( 0x00007f1dcea99310, ), ], }, missing: None, expected_repr: "'molecules', 'properties' or 'crystals'", strict: false, class_repr: "DomainSubmodule", name: "str-enum[DomainSubmodule]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[DomainSubmodule]]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key: Simple { key: "algorithm_name", py_key: Py( 0x00007f1dcaf8cb30, ), path: LookupPath( [ S( "algorithm_name", Py( 0x00007f1dcaf8cb70, ), ), ], ), }, name_py: Py( 0x00007f1ea52ce770, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1e866fd2b0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key: Simple { key: "algorithm_version", py_key: Py( 0x00007f1dcaf850c0, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f1dcaf85070, ), ), ], ), }, name_py: Py( 0x00007f1ea52ed250, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_key: Simple { key: "algorithm_application", py_key: Py( 0x00007f1dcaf85110, ), path: LookupPath( [ S( "algorithm_application", Py( 0x00007f1dcaf85160, ), ), ], ), }, name_py: Py( 0x00007f1dcf4e0530, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "batch_size", lookup_key: Simple { key: "batch_size", py_key: Py( 0x00007f1dcaf8cbb0, ), path: LookupPath( [ S( "batch_size", Py( 0x00007f1dcaf8cbf0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ccc70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea94690d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "workers", lookup_key: Simple { key: "workers", py_key: Py( 0x00007f1dcaf8cc30, ), path: LookupPath( [ S( "workers", Py( 0x00007f1dcaf8cc70, ), ), ], ), }, name_py: Py( 0x00007f1ea6a99070, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea94681d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "device", lookup_key: Simple { key: "device", py_key: Py( 0x00007f1dcaf8ccb0, ), path: LookupPath( [ S( "device", Py( 0x00007f1dcaf8ccf0, ), ), ], ), }, name_py: Py( 0x00007f1ea7046af0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x000056185a457740, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), name: "nullable[str]", }, ), validate_default: false, copy_default: false, name: "default[nullable[str]]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, ], model_name: "MolformerParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000056186760f0c0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f1ea71db950, ), name: "MolformerParameters", }, ), definitions=[], cache_strings=True)¶: The pydantic-core SchemaValidator used to validate instances of the model.

__signature__: ClassVar[Signature] = <Signature (*, algorithm_type: str = 'prediction', domain: gt4sd.properties.core.DomainSubmodule = <DomainSubmodule.molecules: 'molecules'>, algorithm_name: str = 'molformer', algorithm_version: str, algorithm_application: str, batch_size: int = 128, workers: int = 8, device: Optional[str] = None) -> None>¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

_abc_impl = <_abc._abc_data object>¶

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'algorithm_application': FieldInfo(annotation=str, required=True, examples=['Tox21']), 'algorithm_name': FieldInfo(annotation=str, required=False, default='molformer'), 'algorithm_type': FieldInfo(annotation=str, required=False, default='prediction'), 'algorithm_version': FieldInfo(annotation=str, required=True, description='Version of the algorithm', examples=['v0']), 'batch_size': FieldInfo(annotation=int, required=False, default=128, description='Prediction batch size'), 'device': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Device to be used for inference'), 'domain': FieldInfo(annotation=DomainSubmodule, required=False, default=<DomainSubmodule.molecules: 'molecules'>), 'workers': FieldInfo(annotation=int, required=False, default=8, description='Number of data loading workers')}¶

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

class MolformerClassificationParameters(**data)[source]¶

Bases: MolformerParameters

algorithm_application: str¶

__dict__¶

__pydantic_fields_set__: set[str]¶: The names of fields explicitly set during instantiation.

__pydantic_extra__: dict[str, Any] | None¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_private__: dict[str, Any] | None¶: Values of private attributes set on the model instance.

__abstractmethods__ = frozenset({})¶

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': <class 'str'>, 'algorithm_name': 'str', 'algorithm_type': 'str', 'algorithm_version': 'str', 'batch_size': 'int', 'device': 'Optional[str]', 'domain': 'DomainSubmodule', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'workers': 'int'}¶

__class_vars__: ClassVar[set[str]] = {}¶: The names of the class variables defined on the model.

__doc__ = None¶

__module__ = 'gt4sd.properties.molecules.core'¶

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.molecules.core.MolformerClassificationParameters'>, 'config': {'title': 'MolformerClassificationParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.properties.molecules.core.MolformerClassificationParameters'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.molecules.core.MolformerClassificationParameters'>>]}, 'ref': 'gt4sd.properties.molecules.core.MolformerClassificationParameters:94662814458560', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'classification', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'molformer', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'batch_size': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 128, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'device': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'str'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'domain': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': DomainSubmodule.molecules, 'schema': {'cls': <enum 'DomainSubmodule'>, 'members': [<DomainSubmodule.molecules: 'molecules'>, <DomainSubmodule.properties: 'properties'>, <DomainSubmodule.crystals: 'crystals'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.core.DomainSubmodule:94662808869760', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'workers': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 8, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'MolformerClassificationParameters', 'type': 'model-fields'}, 'type': 'model'}¶: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False¶: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶: The name of the post-init method for the model, if defined.

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x00005618676de2c0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "domain": SerField { key_py: Py( 0x00007f1ea8dd1270, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dcea99230, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000561867189b80, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, }, "algorithm_application": SerField { key_py: Py( 0x00007f1dcf4e0530, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1e3398ab30, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "workers": SerField { key_py: Py( 0x00007f1ea6a99070, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea94681d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "algorithm_type": SerField { key_py: Py( 0x00007f1ea52cf4f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dea152330, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_name": SerField { key_py: Py( 0x00007f1ea52ce770, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1e866fd2b0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_version": SerField { key_py: Py( 0x00007f1ea52ed250, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "batch_size": SerField { key_py: Py( 0x00007f1ea52ccc70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea94690d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "device": SerField { key_py: Py( 0x00007f1ea7046af0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x000056185a457740, ), ), serializer: Nullable( NullableSerializer { serializer: Str( StrSerializer, ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 8, }, ), has_extra: false, root_model: false, name: "MolformerClassificationParameters", }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="MolformerClassificationParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_key: Simple { key: "algorithm_type", py_key: Py( 0x00007f1dcaf8f270, ), path: LookupPath( [ S( "algorithm_type", Py( 0x00007f1dcaf8f1f0, ), ), ], ), }, name_py: Py( 0x00007f1ea52cf4f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dea152330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007f1dcaf8f1b0, ), path: LookupPath( [ S( "domain", Py( 0x00007f1dcaf8f170, ), ), ], ), }, name_py: Py( 0x00007f1ea8dd1270, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dcea99230, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000561867189b80, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "molecules": 0, "crystals": 2, "properties": 1, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f1dcea99230, ), Py( 0x00007f1dcea992a0, ), Py( 0x00007f1dcea99310, ), ], }, missing: None, expected_repr: "'molecules', 'properties' or 'crystals'", strict: false, class_repr: "DomainSubmodule", name: "str-enum[DomainSubmodule]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[DomainSubmodule]]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key: Simple { key: "algorithm_name", py_key: Py( 0x00007f1dcaf8f230, ), path: LookupPath( [ S( "algorithm_name", Py( 0x00007f1dcaf8f2b0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ce770, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1e866fd2b0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key: Simple { key: "algorithm_version", py_key: Py( 0x00007f1dcaf853e0, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f1dcaf85390, ), ), ], ), }, name_py: Py( 0x00007f1ea52ed250, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_key: Simple { key: "algorithm_application", py_key: Py( 0x00007f1dcaf85430, ), path: LookupPath( [ S( "algorithm_application", Py( 0x00007f1dcaf85480, ), ), ], ), }, name_py: Py( 0x00007f1dcf4e0530, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1e3398ab30, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_key: Simple { key: "batch_size", py_key: Py( 0x00007f1dcaf8f330, ), path: LookupPath( [ S( "batch_size", Py( 0x00007f1dcaf8f2f0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ccc70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea94690d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "workers", lookup_key: Simple { key: "workers", py_key: Py( 0x00007f1dcaf8f370, ), path: LookupPath( [ S( "workers", Py( 0x00007f1dcaf8f3b0, ), ), ], ), }, name_py: Py( 0x00007f1ea6a99070, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea94681d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "device", lookup_key: Simple { key: "device", py_key: Py( 0x00007f1dcaf8f3f0, ), path: LookupPath( [ S( "device", Py( 0x00007f1dcaf8f430, ), ), ], ), }, name_py: Py( 0x00007f1ea7046af0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x000056185a457740, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), name: "nullable[str]", }, ), validate_default: false, copy_default: false, name: "default[nullable[str]]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, ], model_name: "MolformerClassificationParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x00005618676de2c0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f1ea71db950, ), name: "MolformerClassificationParameters", }, ), definitions=[], cache_strings=True)¶: The pydantic-core SchemaValidator used to validate instances of the model.

__signature__: ClassVar[Signature] = <Signature (*, algorithm_type: str = 'prediction', domain: gt4sd.properties.core.DomainSubmodule = <DomainSubmodule.molecules: 'molecules'>, algorithm_name: str = 'molformer', algorithm_version: str, algorithm_application: str = 'classification', batch_size: int = 128, workers: int = 8, device: Optional[str] = None) -> None>¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

_abc_impl = <_abc._abc_data object>¶

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'algorithm_application': FieldInfo(annotation=str, required=False, default='classification'), 'algorithm_name': FieldInfo(annotation=str, required=False, default='molformer'), 'algorithm_type': FieldInfo(annotation=str, required=False, default='prediction'), 'algorithm_version': FieldInfo(annotation=str, required=True, description='Version of the algorithm', examples=['v0']), 'batch_size': FieldInfo(annotation=int, required=False, default=128, description='Prediction batch size'), 'device': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Device to be used for inference'), 'domain': FieldInfo(annotation=DomainSubmodule, required=False, default=<DomainSubmodule.molecules: 'molecules'>), 'workers': FieldInfo(annotation=int, required=False, default=8, description='Number of data loading workers')}¶

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

class MolformerMultitaskClassificationParameters(**data)[source]¶

Bases: MolformerParameters

algorithm_application: str¶

__dict__¶

__pydantic_fields_set__: set[str]¶: The names of fields explicitly set during instantiation.

__pydantic_extra__: dict[str, Any] | None¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_private__: dict[str, Any] | None¶: Values of private attributes set on the model instance.

__abstractmethods__ = frozenset({})¶

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': <class 'str'>, 'algorithm_name': 'str', 'algorithm_type': 'str', 'algorithm_version': 'str', 'batch_size': 'int', 'device': 'Optional[str]', 'domain': 'DomainSubmodule', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'workers': 'int'}¶

__class_vars__: ClassVar[set[str]] = {}¶: The names of the class variables defined on the model.

__doc__ = None¶

__module__ = 'gt4sd.properties.molecules.core'¶

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.molecules.core.MolformerMultitaskClassificationParameters'>, 'config': {'title': 'MolformerMultitaskClassificationParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.properties.molecules.core.MolformerMultitaskClassificationParameters'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.molecules.core.MolformerMultitaskClassificationParameters'>>]}, 'ref': 'gt4sd.properties.molecules.core.MolformerMultitaskClassificationParameters:94662815036672', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'multitask_classification', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'molformer', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'batch_size': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 128, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'device': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'str'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'domain': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': DomainSubmodule.molecules, 'schema': {'cls': <enum 'DomainSubmodule'>, 'members': [<DomainSubmodule.molecules: 'molecules'>, <DomainSubmodule.properties: 'properties'>, <DomainSubmodule.crystals: 'crystals'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.core.DomainSubmodule:94662808869760', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'workers': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 8, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'MolformerMultitaskClassificationParameters', 'type': 'model-fields'}, 'type': 'model'}¶: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False¶: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶: The name of the post-init method for the model, if defined.

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000056186776b500, ), serializer: Fields( GeneralFieldsSerializer { fields: { "workers": SerField { key_py: Py( 0x00007f1ea6a99070, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea94681d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "algorithm_name": SerField { key_py: Py( 0x00007f1ea52ce770, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1e866fd2b0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_version": SerField { key_py: Py( 0x00007f1ea52ed250, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "device": SerField { key_py: Py( 0x00007f1ea7046af0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x000056185a457740, ), ), serializer: Nullable( NullableSerializer { serializer: Str( StrSerializer, ), }, ), }, ), ), required: true, }, "batch_size": SerField { key_py: Py( 0x00007f1ea52ccc70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea94690d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "algorithm_application": SerField { key_py: Py( 0x00007f1dcf4e0530, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dd376a330, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_type": SerField { key_py: Py( 0x00007f1ea52cf4f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dea152330, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "domain": SerField { key_py: Py( 0x00007f1ea8dd1270, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dcea99230, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000561867189b80, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 8, }, ), has_extra: false, root_model: false, name: "MolformerMultitaskClassificationParameters", }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="MolformerMultitaskClassificationParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_key: Simple { key: "algorithm_type", py_key: Py( 0x00007f1dcaf675b0, ), path: LookupPath( [ S( "algorithm_type", Py( 0x00007f1dcaf67fb0, ), ), ], ), }, name_py: Py( 0x00007f1ea52cf4f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dea152330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007f1dcaf67f30, ), path: LookupPath( [ S( "domain", Py( 0x00007f1dcaf67f70, ), ), ], ), }, name_py: Py( 0x00007f1ea8dd1270, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dcea99230, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000561867189b80, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "molecules": 0, "properties": 1, "crystals": 2, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f1dcea99230, ), Py( 0x00007f1dcea992a0, ), Py( 0x00007f1dcea99310, ), ], }, missing: None, expected_repr: "'molecules', 'properties' or 'crystals'", strict: false, class_repr: "DomainSubmodule", name: "str-enum[DomainSubmodule]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[DomainSubmodule]]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key: Simple { key: "algorithm_name", py_key: Py( 0x00007f1dce920470, ), path: LookupPath( [ S( "algorithm_name", Py( 0x00007f1dcdc0b0b0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ce770, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1e866fd2b0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key: Simple { key: "algorithm_version", py_key: Py( 0x00007f1dcaf85660, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f1dcaf852f0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ed250, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_key: Simple { key: "algorithm_application", py_key: Py( 0x00007f1dcaf85610, ), path: LookupPath( [ S( "algorithm_application", Py( 0x00007f1dcaf85340, ), ), ], ), }, name_py: Py( 0x00007f1dcf4e0530, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dd376a330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_key: Simple { key: "batch_size", py_key: Py( 0x00007f1dcaddebf0, ), path: LookupPath( [ S( "batch_size", Py( 0x00007f1dcaddeeb0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ccc70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea94690d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "workers", lookup_key: Simple { key: "workers", py_key: Py( 0x00007f1dcadde7f0, ), path: LookupPath( [ S( "workers", Py( 0x00007f1dcadde9f0, ), ), ], ), }, name_py: Py( 0x00007f1ea6a99070, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea94681d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "device", lookup_key: Simple { key: "device", py_key: Py( 0x00007f1dcadde930, ), path: LookupPath( [ S( "device", Py( 0x00007f1dcaddec70, ), ), ], ), }, name_py: Py( 0x00007f1ea7046af0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x000056185a457740, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), name: "nullable[str]", }, ), validate_default: false, copy_default: false, name: "default[nullable[str]]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, ], model_name: "MolformerMultitaskClassificationParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000056186776b500, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f1ea71db950, ), name: "MolformerMultitaskClassificationParameters", }, ), definitions=[], cache_strings=True)¶: The pydantic-core SchemaValidator used to validate instances of the model.

__signature__: ClassVar[Signature] = <Signature (*, algorithm_type: str = 'prediction', domain: gt4sd.properties.core.DomainSubmodule = <DomainSubmodule.molecules: 'molecules'>, algorithm_name: str = 'molformer', algorithm_version: str, algorithm_application: str = 'multitask_classification', batch_size: int = 128, workers: int = 8, device: Optional[str] = None) -> None>¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

_abc_impl = <_abc._abc_data object>¶

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'algorithm_application': FieldInfo(annotation=str, required=False, default='multitask_classification'), 'algorithm_name': FieldInfo(annotation=str, required=False, default='molformer'), 'algorithm_type': FieldInfo(annotation=str, required=False, default='prediction'), 'algorithm_version': FieldInfo(annotation=str, required=True, description='Version of the algorithm', examples=['v0']), 'batch_size': FieldInfo(annotation=int, required=False, default=128, description='Prediction batch size'), 'device': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Device to be used for inference'), 'domain': FieldInfo(annotation=DomainSubmodule, required=False, default=<DomainSubmodule.molecules: 'molecules'>), 'workers': FieldInfo(annotation=int, required=False, default=8, description='Number of data loading workers')}¶

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

class MolformerRegressionParameters(**data)[source]¶

Bases: MolformerParameters

algorithm_application: str¶

__dict__¶

__pydantic_fields_set__: set[str]¶: The names of fields explicitly set during instantiation.

__pydantic_extra__: dict[str, Any] | None¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_private__: dict[str, Any] | None¶: Values of private attributes set on the model instance.

__abstractmethods__ = frozenset({})¶

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': <class 'str'>, 'algorithm_name': 'str', 'algorithm_type': 'str', 'algorithm_version': 'str', 'batch_size': 'int', 'device': 'Optional[str]', 'domain': 'DomainSubmodule', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'workers': 'int'}¶

__class_vars__: ClassVar[set[str]] = {}¶: The names of the class variables defined on the model.

__doc__ = None¶

__module__ = 'gt4sd.properties.molecules.core'¶

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.molecules.core.MolformerRegressionParameters'>, 'config': {'title': 'MolformerRegressionParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.properties.molecules.core.MolformerRegressionParameters'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.molecules.core.MolformerRegressionParameters'>>]}, 'ref': 'gt4sd.properties.molecules.core.MolformerRegressionParameters:94662814544688', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'regression', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'molformer', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'batch_size': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 128, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'device': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'str'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'domain': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': DomainSubmodule.molecules, 'schema': {'cls': <enum 'DomainSubmodule'>, 'members': [<DomainSubmodule.molecules: 'molecules'>, <DomainSubmodule.properties: 'properties'>, <DomainSubmodule.crystals: 'crystals'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.core.DomainSubmodule:94662808869760', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'workers': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 8, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'MolformerRegressionParameters', 'type': 'model-fields'}, 'type': 'model'}¶: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False¶: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶: The name of the post-init method for the model, if defined.

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x00005618676f3330, ), serializer: Fields( GeneralFieldsSerializer { fields: { "batch_size": SerField { key_py: Py( 0x00007f1ea52ccc70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea94690d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "domain": SerField { key_py: Py( 0x00007f1ea8dd1270, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dcea99230, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000561867189b80, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, }, "algorithm_type": SerField { key_py: Py( 0x00007f1ea52cf4f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dea152330, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_name": SerField { key_py: Py( 0x00007f1ea52ce770, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1e866fd2b0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_version": SerField { key_py: Py( 0x00007f1ea52ed250, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "algorithm_application": SerField { key_py: Py( 0x00007f1dcf4e0530, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1de570f1b0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "device": SerField { key_py: Py( 0x00007f1ea7046af0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x000056185a457740, ), ), serializer: Nullable( NullableSerializer { serializer: Str( StrSerializer, ), }, ), }, ), ), required: true, }, "workers": SerField { key_py: Py( 0x00007f1ea6a99070, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea94681d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 8, }, ), has_extra: false, root_model: false, name: "MolformerRegressionParameters", }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="MolformerRegressionParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_key: Simple { key: "algorithm_type", py_key: Py( 0x00007f1dcadd2270, ), path: LookupPath( [ S( "algorithm_type", Py( 0x00007f1dcadd2230, ), ), ], ), }, name_py: Py( 0x00007f1ea52cf4f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dea152330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007f1dcadd21f0, ), path: LookupPath( [ S( "domain", Py( 0x00007f1dcadd21b0, ), ), ], ), }, name_py: Py( 0x00007f1ea8dd1270, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dcea99230, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000561867189b80, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "properties": 1, "crystals": 2, "molecules": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f1dcea99230, ), Py( 0x00007f1dcea992a0, ), Py( 0x00007f1dcea99310, ), ], }, missing: None, expected_repr: "'molecules', 'properties' or 'crystals'", strict: false, class_repr: "DomainSubmodule", name: "str-enum[DomainSubmodule]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[DomainSubmodule]]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key: Simple { key: "algorithm_name", py_key: Py( 0x00007f1dcadd22b0, ), path: LookupPath( [ S( "algorithm_name", Py( 0x00007f1dcadd22f0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ce770, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1e866fd2b0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key: Simple { key: "algorithm_version", py_key: Py( 0x00007f1dcaf85840, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f1dcaf857f0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ed250, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_key: Simple { key: "algorithm_application", py_key: Py( 0x00007f1dcaf85890, ), path: LookupPath( [ S( "algorithm_application", Py( 0x00007f1dcaf858e0, ), ), ], ), }, name_py: Py( 0x00007f1dcf4e0530, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1de570f1b0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_key: Simple { key: "batch_size", py_key: Py( 0x00007f1dcadd2330, ), path: LookupPath( [ S( "batch_size", Py( 0x00007f1dcadd2370, ), ), ], ), }, name_py: Py( 0x00007f1ea52ccc70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea94690d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "workers", lookup_key: Simple { key: "workers", py_key: Py( 0x00007f1dcadd23b0, ), path: LookupPath( [ S( "workers", Py( 0x00007f1dcadd23f0, ), ), ], ), }, name_py: Py( 0x00007f1ea6a99070, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea94681d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "device", lookup_key: Simple { key: "device", py_key: Py( 0x00007f1dcadd2430, ), path: LookupPath( [ S( "device", Py( 0x00007f1dcadd2470, ), ), ], ), }, name_py: Py( 0x00007f1ea7046af0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x000056185a457740, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), name: "nullable[str]", }, ), validate_default: false, copy_default: false, name: "default[nullable[str]]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, ], model_name: "MolformerRegressionParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x00005618676f3330, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f1ea71db950, ), name: "MolformerRegressionParameters", }, ), definitions=[], cache_strings=True)¶: The pydantic-core SchemaValidator used to validate instances of the model.

__signature__: ClassVar[Signature] = <Signature (*, algorithm_type: str = 'prediction', domain: gt4sd.properties.core.DomainSubmodule = <DomainSubmodule.molecules: 'molecules'>, algorithm_name: str = 'molformer', algorithm_version: str, algorithm_application: str = 'regression', batch_size: int = 128, workers: int = 8, device: Optional[str] = None) -> None>¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

_abc_impl = <_abc._abc_data object>¶

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'algorithm_application': FieldInfo(annotation=str, required=False, default='regression'), 'algorithm_name': FieldInfo(annotation=str, required=False, default='molformer'), 'algorithm_type': FieldInfo(annotation=str, required=False, default='prediction'), 'algorithm_version': FieldInfo(annotation=str, required=True, description='Version of the algorithm', examples=['v0']), 'batch_size': FieldInfo(annotation=int, required=False, default=128, description='Prediction batch size'), 'device': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Device to be used for inference'), 'domain': FieldInfo(annotation=DomainSubmodule, required=False, default=<DomainSubmodule.molecules: 'molecules'>), 'workers': FieldInfo(annotation=int, required=False, default=8, description='Number of data loading workers')}¶

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

class MCAParameters(**data)[source]¶

Bases: S3ParametersMolecules

algorithm_name: str¶

__dict__¶

__pydantic_fields_set__: set[str]¶: The names of fields explicitly set during instantiation.

__pydantic_extra__: dict[str, Any] | None¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_private__: dict[str, Any] | None¶: Values of private attributes set on the model instance.

__abstractmethods__ = frozenset({})¶

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': 'str', 'algorithm_name': <class 'str'>, 'algorithm_type': 'str', 'algorithm_version': 'str', 'domain': 'DomainSubmodule', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]'}¶

__class_vars__: ClassVar[set[str]] = {}¶: The names of the class variables defined on the model.

__doc__ = None¶

__module__ = 'gt4sd.properties.molecules.core'¶

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.molecules.core.MCAParameters'>, 'config': {'title': 'MCAParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.properties.molecules.core.MCAParameters'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.molecules.core.MCAParameters'>>]}, 'ref': 'gt4sd.properties.molecules.core.MCAParameters:94662815167744', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'MCA', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'domain': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': DomainSubmodule.molecules, 'schema': {'cls': <enum 'DomainSubmodule'>, 'members': [<DomainSubmodule.molecules: 'molecules'>, <DomainSubmodule.properties: 'properties'>, <DomainSubmodule.crystals: 'crystals'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.core.DomainSubmodule:94662808869760', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'MCAParameters', 'type': 'model-fields'}, 'type': 'model'}¶: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False¶: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶: The name of the post-init method for the model, if defined.

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000056186778b500, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_type": SerField { key_py: Py( 0x00007f1ea52cf4f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dea152330, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_name": SerField { key_py: Py( 0x00007f1ea52ce770, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dce8bc270, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_version": SerField { key_py: Py( 0x00007f1ea52ed250, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "domain": SerField { key_py: Py( 0x00007f1ea8dd1270, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dcea99230, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000561867189b80, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, }, "algorithm_application": SerField { key_py: Py( 0x00007f1dcf4e0530, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 5, }, ), has_extra: false, root_model: false, name: "MCAParameters", }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="MCAParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_key: Simple { key: "algorithm_type", py_key: Py( 0x00007f1dcadd39f0, ), path: LookupPath( [ S( "algorithm_type", Py( 0x00007f1dcadd39b0, ), ), ], ), }, name_py: Py( 0x00007f1ea52cf4f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dea152330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007f1dcadd3970, ), path: LookupPath( [ S( "domain", Py( 0x00007f1dcadd3930, ), ), ], ), }, name_py: Py( 0x00007f1ea8dd1270, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dcea99230, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000561867189b80, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "properties": 1, "crystals": 2, "molecules": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f1dcea99230, ), Py( 0x00007f1dcea992a0, ), Py( 0x00007f1dcea99310, ), ], }, missing: None, expected_repr: "'molecules', 'properties' or 'crystals'", strict: false, class_repr: "DomainSubmodule", name: "str-enum[DomainSubmodule]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[DomainSubmodule]]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key: Simple { key: "algorithm_name", py_key: Py( 0x00007f1dcadd3a30, ), path: LookupPath( [ S( "algorithm_name", Py( 0x00007f1dcadd3a70, ), ), ], ), }, name_py: Py( 0x00007f1ea52ce770, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dce8bc270, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key: Simple { key: "algorithm_version", py_key: Py( 0x00007f1dcaf85b60, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f1dcaf85bb0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ed250, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_key: Simple { key: "algorithm_application", py_key: Py( 0x00007f1dcaf85b10, ), path: LookupPath( [ S( "algorithm_application", Py( 0x00007f1dcaf85c00, ), ), ], ), }, name_py: Py( 0x00007f1dcf4e0530, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, ], model_name: "MCAParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000056186778b500, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f1ea71db950, ), name: "MCAParameters", }, ), definitions=[], cache_strings=True)¶: The pydantic-core SchemaValidator used to validate instances of the model.

__signature__: ClassVar[Signature] = <Signature (*, algorithm_type: str = 'prediction', domain: gt4sd.properties.core.DomainSubmodule = <DomainSubmodule.molecules: 'molecules'>, algorithm_name: str = 'MCA', algorithm_version: str, algorithm_application: str) -> None>¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

_abc_impl = <_abc._abc_data object>¶

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'algorithm_application': FieldInfo(annotation=str, required=True, examples=['Tox21']), 'algorithm_name': FieldInfo(annotation=str, required=False, default='MCA'), 'algorithm_type': FieldInfo(annotation=str, required=False, default='prediction'), 'algorithm_version': FieldInfo(annotation=str, required=True, description='Version of the algorithm', examples=['v0']), 'domain': FieldInfo(annotation=DomainSubmodule, required=False, default=<DomainSubmodule.molecules: 'molecules'>)}¶

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

class Tox21Parameters(**data)[source]¶

Bases: MCAParameters

algorithm_application: str¶

__dict__¶

__pydantic_fields_set__: set[str]¶: The names of fields explicitly set during instantiation.

__pydantic_extra__: dict[str, Any] | None¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_private__: dict[str, Any] | None¶: Values of private attributes set on the model instance.

__abstractmethods__ = frozenset({})¶

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': <class 'str'>, 'algorithm_name': 'str', 'algorithm_type': 'str', 'algorithm_version': 'str', 'domain': 'DomainSubmodule', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]'}¶

__class_vars__: ClassVar[set[str]] = {}¶: The names of the class variables defined on the model.

__doc__ = None¶

__module__ = 'gt4sd.properties.molecules.core'¶

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.molecules.core.Tox21Parameters'>, 'config': {'title': 'Tox21Parameters'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.properties.molecules.core.Tox21Parameters'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.molecules.core.Tox21Parameters'>>]}, 'ref': 'gt4sd.properties.molecules.core.Tox21Parameters:94662814775472', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'Tox21', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'MCA', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'domain': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': DomainSubmodule.molecules, 'schema': {'cls': <enum 'DomainSubmodule'>, 'members': [<DomainSubmodule.molecules: 'molecules'>, <DomainSubmodule.properties: 'properties'>, <DomainSubmodule.crystals: 'crystals'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.core.DomainSubmodule:94662808869760', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'Tox21Parameters', 'type': 'model-fields'}, 'type': 'model'}¶: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False¶: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶: The name of the post-init method for the model, if defined.

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000056186772b8b0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "domain": SerField { key_py: Py( 0x00007f1ea8dd1270, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dcea99230, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000561867189b80, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, }, "algorithm_name": SerField { key_py: Py( 0x00007f1ea52ce770, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dce8bc270, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_version": SerField { key_py: Py( 0x00007f1ea52ed250, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "algorithm_application": SerField { key_py: Py( 0x00007f1dcf4e0530, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dd065c730, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_type": SerField { key_py: Py( 0x00007f1ea52cf4f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dea152330, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 5, }, ), has_extra: false, root_model: false, name: "Tox21Parameters", }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="Tox21Parameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_key: Simple { key: "algorithm_type", py_key: Py( 0x00007f1dcade95f0, ), path: LookupPath( [ S( "algorithm_type", Py( 0x00007f1dcade95b0, ), ), ], ), }, name_py: Py( 0x00007f1ea52cf4f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dea152330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007f1dcade9570, ), path: LookupPath( [ S( "domain", Py( 0x00007f1dcade9530, ), ), ], ), }, name_py: Py( 0x00007f1ea8dd1270, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dcea99230, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000561867189b80, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "crystals": 2, "properties": 1, "molecules": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f1dcea99230, ), Py( 0x00007f1dcea992a0, ), Py( 0x00007f1dcea99310, ), ], }, missing: None, expected_repr: "'molecules', 'properties' or 'crystals'", strict: false, class_repr: "DomainSubmodule", name: "str-enum[DomainSubmodule]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[DomainSubmodule]]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key: Simple { key: "algorithm_name", py_key: Py( 0x00007f1dcade9630, ), path: LookupPath( [ S( "algorithm_name", Py( 0x00007f1dcade9670, ), ), ], ), }, name_py: Py( 0x00007f1ea52ce770, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dce8bc270, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key: Simple { key: "algorithm_version", py_key: Py( 0x00007f1dcaf85e80, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f1dcaf85ed0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ed250, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_key: Simple { key: "algorithm_application", py_key: Py( 0x00007f1dcaf85e30, ), path: LookupPath( [ S( "algorithm_application", Py( 0x00007f1dcaf85f20, ), ), ], ), }, name_py: Py( 0x00007f1dcf4e0530, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dd065c730, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, ], model_name: "Tox21Parameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000056186772b8b0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f1ea71db950, ), name: "Tox21Parameters", }, ), definitions=[], cache_strings=True)¶: The pydantic-core SchemaValidator used to validate instances of the model.

__signature__: ClassVar[Signature] = <Signature (*, algorithm_type: str = 'prediction', domain: gt4sd.properties.core.DomainSubmodule = <DomainSubmodule.molecules: 'molecules'>, algorithm_name: str = 'MCA', algorithm_version: str, algorithm_application: str = 'Tox21') -> None>¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

_abc_impl = <_abc._abc_data object>¶

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'algorithm_application': FieldInfo(annotation=str, required=False, default='Tox21'), 'algorithm_name': FieldInfo(annotation=str, required=False, default='MCA'), 'algorithm_type': FieldInfo(annotation=str, required=False, default='prediction'), 'algorithm_version': FieldInfo(annotation=str, required=True, description='Version of the algorithm', examples=['v0']), 'domain': FieldInfo(annotation=DomainSubmodule, required=False, default=<DomainSubmodule.molecules: 'molecules'>)}¶

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

class ClinToxParameters(**data)[source]¶

Bases: MCAParameters

algorithm_application: str¶

__dict__¶

__pydantic_fields_set__: set[str]¶: The names of fields explicitly set during instantiation.

__pydantic_extra__: dict[str, Any] | None¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_private__: dict[str, Any] | None¶: Values of private attributes set on the model instance.

__abstractmethods__ = frozenset({})¶

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': <class 'str'>, 'algorithm_name': 'str', 'algorithm_type': 'str', 'algorithm_version': 'str', 'domain': 'DomainSubmodule', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]'}¶

__class_vars__: ClassVar[set[str]] = {}¶: The names of the class variables defined on the model.

__doc__ = None¶

__module__ = 'gt4sd.properties.molecules.core'¶

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.molecules.core.ClinToxParameters'>, 'config': {'title': 'ClinToxParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.properties.molecules.core.ClinToxParameters'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.molecules.core.ClinToxParameters'>>]}, 'ref': 'gt4sd.properties.molecules.core.ClinToxParameters:94662815081872', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'ClinTox', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'MCA', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'domain': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': DomainSubmodule.molecules, 'schema': {'cls': <enum 'DomainSubmodule'>, 'members': [<DomainSubmodule.molecules: 'molecules'>, <DomainSubmodule.properties: 'properties'>, <DomainSubmodule.crystals: 'crystals'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.core.DomainSubmodule:94662808869760', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'ClinToxParameters', 'type': 'model-fields'}, 'type': 'model'}¶: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False¶: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶: The name of the post-init method for the model, if defined.

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x0000561867776590, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_version": SerField { key_py: Py( 0x00007f1ea52ed250, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "algorithm_name": SerField { key_py: Py( 0x00007f1ea52ce770, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dce8bc270, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_type": SerField { key_py: Py( 0x00007f1ea52cf4f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dea152330, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_application": SerField { key_py: Py( 0x00007f1dcf4e0530, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dd065c5b0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "domain": SerField { key_py: Py( 0x00007f1ea8dd1270, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dcea99230, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000561867189b80, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 5, }, ), has_extra: false, root_model: false, name: "ClinToxParameters", }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="ClinToxParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_key: Simple { key: "algorithm_type", py_key: Py( 0x00007f1dcc3c4d70, ), path: LookupPath( [ S( "algorithm_type", Py( 0x00007f1dcaf8e3f0, ), ), ], ), }, name_py: Py( 0x00007f1ea52cf4f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dea152330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007f1dcaf8fbf0, ), path: LookupPath( [ S( "domain", Py( 0x00007f1dcaf8faf0, ), ), ], ), }, name_py: Py( 0x00007f1ea8dd1270, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dcea99230, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000561867189b80, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "properties": 1, "crystals": 2, "molecules": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f1dcea99230, ), Py( 0x00007f1dcea992a0, ), Py( 0x00007f1dcea99310, ), ], }, missing: None, expected_repr: "'molecules', 'properties' or 'crystals'", strict: false, class_repr: "DomainSubmodule", name: "str-enum[DomainSubmodule]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[DomainSubmodule]]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key: Simple { key: "algorithm_name", py_key: Py( 0x00007f1dcaf8dcf0, ), path: LookupPath( [ S( "algorithm_name", Py( 0x00007f1dcaf679f0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ce770, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dce8bc270, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key: Simple { key: "algorithm_version", py_key: Py( 0x00007f1dcaf85d90, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f1dcaf85de0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ed250, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_key: Simple { key: "algorithm_application", py_key: Py( 0x00007f1dcaf860b0, ), path: LookupPath( [ S( "algorithm_application", Py( 0x00007f1dcaf85a70, ), ), ], ), }, name_py: Py( 0x00007f1dcf4e0530, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dd065c5b0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, ], model_name: "ClinToxParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x0000561867776590, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f1ea71db950, ), name: "ClinToxParameters", }, ), definitions=[], cache_strings=True)¶: The pydantic-core SchemaValidator used to validate instances of the model.

__signature__: ClassVar[Signature] = <Signature (*, algorithm_type: str = 'prediction', domain: gt4sd.properties.core.DomainSubmodule = <DomainSubmodule.molecules: 'molecules'>, algorithm_name: str = 'MCA', algorithm_version: str, algorithm_application: str = 'ClinTox') -> None>¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

_abc_impl = <_abc._abc_data object>¶

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'algorithm_application': FieldInfo(annotation=str, required=False, default='ClinTox'), 'algorithm_name': FieldInfo(annotation=str, required=False, default='MCA'), 'algorithm_type': FieldInfo(annotation=str, required=False, default='prediction'), 'algorithm_version': FieldInfo(annotation=str, required=True, description='Version of the algorithm', examples=['v0']), 'domain': FieldInfo(annotation=DomainSubmodule, required=False, default=<DomainSubmodule.molecules: 'molecules'>)}¶

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

class SiderParameters(**data)[source]¶

Bases: MCAParameters

algorithm_application: str¶

__dict__¶

__pydantic_fields_set__: set[str]¶: The names of fields explicitly set during instantiation.

__pydantic_extra__: dict[str, Any] | None¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_private__: dict[str, Any] | None¶: Values of private attributes set on the model instance.

__abstractmethods__ = frozenset({})¶

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': <class 'str'>, 'algorithm_name': 'str', 'algorithm_type': 'str', 'algorithm_version': 'str', 'domain': 'DomainSubmodule', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]'}¶

__class_vars__: ClassVar[set[str]] = {}¶: The names of the class variables defined on the model.

__doc__ = None¶

__module__ = 'gt4sd.properties.molecules.core'¶

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.molecules.core.SiderParameters'>, 'config': {'title': 'SiderParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.properties.molecules.core.SiderParameters'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.molecules.core.SiderParameters'>>]}, 'ref': 'gt4sd.properties.molecules.core.SiderParameters:94662814921232', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'SIDER', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'MCA', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'domain': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': DomainSubmodule.molecules, 'schema': {'cls': <enum 'DomainSubmodule'>, 'members': [<DomainSubmodule.molecules: 'molecules'>, <DomainSubmodule.properties: 'properties'>, <DomainSubmodule.crystals: 'crystals'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.core.DomainSubmodule:94662808869760', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'SiderParameters', 'type': 'model-fields'}, 'type': 'model'}¶: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False¶: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶: The name of the post-init method for the model, if defined.

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000056186774f210, ), serializer: Fields( GeneralFieldsSerializer { fields: { "domain": SerField { key_py: Py( 0x00007f1ea8dd1270, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dcea99230, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000561867189b80, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, }, "algorithm_type": SerField { key_py: Py( 0x00007f1ea52cf4f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dea152330, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_name": SerField { key_py: Py( 0x00007f1ea52ce770, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dce8bc270, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_version": SerField { key_py: Py( 0x00007f1ea52ed250, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "algorithm_application": SerField { key_py: Py( 0x00007f1dcf4e0530, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dd065c530, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 5, }, ), has_extra: false, root_model: false, name: "SiderParameters", }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="SiderParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_key: Simple { key: "algorithm_type", py_key: Py( 0x00007f1dcadea7f0, ), path: LookupPath( [ S( "algorithm_type", Py( 0x00007f1dcadea7b0, ), ), ], ), }, name_py: Py( 0x00007f1ea52cf4f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dea152330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007f1dcadea770, ), path: LookupPath( [ S( "domain", Py( 0x00007f1dcadea730, ), ), ], ), }, name_py: Py( 0x00007f1ea8dd1270, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dcea99230, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000561867189b80, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "molecules": 0, "properties": 1, "crystals": 2, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f1dcea99230, ), Py( 0x00007f1dcea992a0, ), Py( 0x00007f1dcea99310, ), ], }, missing: None, expected_repr: "'molecules', 'properties' or 'crystals'", strict: false, class_repr: "DomainSubmodule", name: "str-enum[DomainSubmodule]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[DomainSubmodule]]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key: Simple { key: "algorithm_name", py_key: Py( 0x00007f1dcadea830, ), path: LookupPath( [ S( "algorithm_name", Py( 0x00007f1dcadea870, ), ), ], ), }, name_py: Py( 0x00007f1ea52ce770, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dce8bc270, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key: Simple { key: "algorithm_version", py_key: Py( 0x00007f1dcaf86240, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f1dcaf86290, ), ), ], ), }, name_py: Py( 0x00007f1ea52ed250, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_key: Simple { key: "algorithm_application", py_key: Py( 0x00007f1dcaf861f0, ), path: LookupPath( [ S( "algorithm_application", Py( 0x00007f1dcaf862e0, ), ), ], ), }, name_py: Py( 0x00007f1dcf4e0530, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dd065c530, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, ], model_name: "SiderParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000056186774f210, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f1ea71db950, ), name: "SiderParameters", }, ), definitions=[], cache_strings=True)¶: The pydantic-core SchemaValidator used to validate instances of the model.

__signature__: ClassVar[Signature] = <Signature (*, algorithm_type: str = 'prediction', domain: gt4sd.properties.core.DomainSubmodule = <DomainSubmodule.molecules: 'molecules'>, algorithm_name: str = 'MCA', algorithm_version: str, algorithm_application: str = 'SIDER') -> None>¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

_abc_impl = <_abc._abc_data object>¶

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'algorithm_application': FieldInfo(annotation=str, required=False, default='SIDER'), 'algorithm_name': FieldInfo(annotation=str, required=False, default='MCA'), 'algorithm_type': FieldInfo(annotation=str, required=False, default='prediction'), 'algorithm_version': FieldInfo(annotation=str, required=True, description='Version of the algorithm', examples=['v0']), 'domain': FieldInfo(annotation=DomainSubmodule, required=False, default=<DomainSubmodule.molecules: 'molecules'>)}¶

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

class OrganToxParameters(**data)[source]¶

Bases: MCAParameters

class Organs(value)[source]¶

Bases: str, Enum

An enumeration.

adrenal_gland: str = 'Adrenal Gland'¶

bone_marrow: str = 'Bone Marrow'¶

brain: str = 'Brain'¶

eye: str = 'Eye'¶

heart: str = 'Heart'¶

kidney: str = 'Kidney'¶

liver: str = 'Liver'¶

lung: str = 'Lung'¶

lymph_node: str = 'Lymph Node'¶

mammary_gland: str = 'Mammary Gland'¶

ovary: str = 'Ovary'¶

pancreas: str = 'Pancreas'¶

pituitary_gland: str = 'Pituitary Gland'¶

spleen: str = 'Spleen'¶

stomach: str = 'Stomach'¶

testes: str = 'Testes'¶

thymus: str = 'Thymus'¶

thyroid_gland: str = 'Thyroid Gland'¶

urinary_bladder: str = 'Urinary Bladder'¶

uterus: str = 'Uterus'¶

_generate_next_value_(start, count, last_values)¶

Generate the next value when not given.

name: the name of the member start: the initial start value or None count: the number of existing members last_value: the last value assigned or None

__module__ = 'gt4sd.properties.molecules.core'¶

__annotations__ = {'adrenal_gland': <class 'str'>, 'bone_marrow': <class 'str'>, 'brain': <class 'str'>, 'eye': <class 'str'>, 'heart': <class 'str'>, 'kidney': <class 'str'>, 'liver': <class 'str'>, 'lung': <class 'str'>, 'lymph_node': <class 'str'>, 'mammary_gland': <class 'str'>, 'ovary': <class 'str'>, 'pancreas': <class 'str'>, 'pituitary_gland': <class 'str'>, 'spleen': <class 'str'>, 'stomach': <class 'str'>, 'testes': <class 'str'>, 'thymus': <class 'str'>, 'thyroid_gland': <class 'str'>, 'urinary_bladder': <class 'str'>, 'uterus': <class 'str'>}¶

__dict__ = mappingproxy({'_generate_next_value_': <function Enum._generate_next_value_>, '__module__': 'gt4sd.properties.molecules.core', '__annotations__': {'adrenal_gland': <class 'str'>, 'bone_marrow': <class 'str'>, 'brain': <class 'str'>, 'eye': <class 'str'>, 'heart': <class 'str'>, 'kidney': <class 'str'>, 'liver': <class 'str'>, 'lung': <class 'str'>, 'lymph_node': <class 'str'>, 'mammary_gland': <class 'str'>, 'ovary': <class 'str'>, 'pancreas': <class 'str'>, 'pituitary_gland': <class 'str'>, 'spleen': <class 'str'>, 'stomach': <class 'str'>, 'testes': <class 'str'>, 'thymus': <class 'str'>, 'thyroid_gland': <class 'str'>, 'urinary_bladder': <class 'str'>, 'uterus': <class 'str'>}, '__doc__': 'An enumeration.', '__dict__': <attribute '__dict__' of 'Organs' objects>, '__weakref__': <attribute '__weakref__' of 'Organs' objects>, '_member_names_': ['adrenal_gland', 'bone_marrow', 'brain', 'eye', 'heart', 'kidney', 'liver', 'lung', 'lymph_node', 'mammary_gland', 'ovary', 'pancreas', 'pituitary_gland', 'spleen', 'stomach', 'testes', 'thymus', 'thyroid_gland', 'urinary_bladder', 'uterus'], '_member_map_': {'adrenal_gland': <Organs.adrenal_gland: 'Adrenal Gland'>, 'bone_marrow': <Organs.bone_marrow: 'Bone Marrow'>, 'brain': <Organs.brain: 'Brain'>, 'eye': <Organs.eye: 'Eye'>, 'heart': <Organs.heart: 'Heart'>, 'kidney': <Organs.kidney: 'Kidney'>, 'liver': <Organs.liver: 'Liver'>, 'lung': <Organs.lung: 'Lung'>, 'lymph_node': <Organs.lymph_node: 'Lymph Node'>, 'mammary_gland': <Organs.mammary_gland: 'Mammary Gland'>, 'ovary': <Organs.ovary: 'Ovary'>, 'pancreas': <Organs.pancreas: 'Pancreas'>, 'pituitary_gland': <Organs.pituitary_gland: 'Pituitary Gland'>, 'spleen': <Organs.spleen: 'Spleen'>, 'stomach': <Organs.stomach: 'Stomach'>, 'testes': <Organs.testes: 'Testes'>, 'thymus': <Organs.thymus: 'Thymus'>, 'thyroid_gland': <Organs.thyroid_gland: 'Thyroid Gland'>, 'urinary_bladder': <Organs.urinary_bladder: 'Urinary Bladder'>, 'uterus': <Organs.uterus: 'Uterus'>}, '_member_type_': <class 'str'>, '_value2member_map_': {'Adrenal Gland': <Organs.adrenal_gland: 'Adrenal Gland'>, 'Bone Marrow': <Organs.bone_marrow: 'Bone Marrow'>, 'Brain': <Organs.brain: 'Brain'>, 'Eye': <Organs.eye: 'Eye'>, 'Heart': <Organs.heart: 'Heart'>, 'Kidney': <Organs.kidney: 'Kidney'>, 'Liver': <Organs.liver: 'Liver'>, 'Lung': <Organs.lung: 'Lung'>, 'Lymph Node': <Organs.lymph_node: 'Lymph Node'>, 'Mammary Gland': <Organs.mammary_gland: 'Mammary Gland'>, 'Ovary': <Organs.ovary: 'Ovary'>, 'Pancreas': <Organs.pancreas: 'Pancreas'>, 'Pituitary Gland': <Organs.pituitary_gland: 'Pituitary Gland'>, 'Spleen': <Organs.spleen: 'Spleen'>, 'Stomach': <Organs.stomach: 'Stomach'>, 'Testes': <Organs.testes: 'Testes'>, 'Thymus': <Organs.thymus: 'Thymus'>, 'Thyroid Gland': <Organs.thyroid_gland: 'Thyroid Gland'>, 'Urinary Bladder': <Organs.urinary_bladder: 'Urinary Bladder'>, 'Uterus': <Organs.uterus: 'Uterus'>}, 'adrenal_gland': <Organs.adrenal_gland: 'Adrenal Gland'>, 'bone_marrow': <Organs.bone_marrow: 'Bone Marrow'>, 'brain': <Organs.brain: 'Brain'>, 'eye': <Organs.eye: 'Eye'>, 'heart': <Organs.heart: 'Heart'>, 'kidney': <Organs.kidney: 'Kidney'>, 'liver': <Organs.liver: 'Liver'>, 'lung': <Organs.lung: 'Lung'>, 'lymph_node': <Organs.lymph_node: 'Lymph Node'>, 'mammary_gland': <Organs.mammary_gland: 'Mammary Gland'>, 'ovary': <Organs.ovary: 'Ovary'>, 'pancreas': <Organs.pancreas: 'Pancreas'>, 'pituitary_gland': <Organs.pituitary_gland: 'Pituitary Gland'>, 'spleen': <Organs.spleen: 'Spleen'>, 'stomach': <Organs.stomach: 'Stomach'>, 'testes': <Organs.testes: 'Testes'>, 'thymus': <Organs.thymus: 'Thymus'>, 'thyroid_gland': <Organs.thyroid_gland: 'Thyroid Gland'>, 'urinary_bladder': <Organs.urinary_bladder: 'Urinary Bladder'>, 'uterus': <Organs.uterus: 'Uterus'>, '__repr__': <function Enum.__repr__>, '__str__': <function Enum.__str__>, '__format__': <function Enum.__format__>, '__new__': <function Enum.__new__>})¶

__weakref__¶: list of weak references to the object (if defined)

_member_names_ = ['adrenal_gland', 'bone_marrow', 'brain', 'eye', 'heart', 'kidney', 'liver', 'lung', 'lymph_node', 'mammary_gland', 'ovary', 'pancreas', 'pituitary_gland', 'spleen', 'stomach', 'testes', 'thymus', 'thyroid_gland', 'urinary_bladder', 'uterus']¶

_member_map_ = {'adrenal_gland': Organs.adrenal_gland, 'bone_marrow': Organs.bone_marrow, 'brain': Organs.brain, 'eye': Organs.eye, 'heart': Organs.heart, 'kidney': Organs.kidney, 'liver': Organs.liver, 'lung': Organs.lung, 'lymph_node': Organs.lymph_node, 'mammary_gland': Organs.mammary_gland, 'ovary': Organs.ovary, 'pancreas': Organs.pancreas, 'pituitary_gland': Organs.pituitary_gland, 'spleen': Organs.spleen, 'stomach': Organs.stomach, 'testes': Organs.testes, 'thymus': Organs.thymus, 'thyroid_gland': Organs.thyroid_gland, 'urinary_bladder': Organs.urinary_bladder, 'uterus': Organs.uterus}¶

_member_type_¶: alias of str

_value2member_map_ = {'Adrenal Gland': Organs.adrenal_gland, 'Bone Marrow': Organs.bone_marrow, 'Brain': Organs.brain, 'Eye': Organs.eye, 'Heart': Organs.heart, 'Kidney': Organs.kidney, 'Liver': Organs.liver, 'Lung': Organs.lung, 'Lymph Node': Organs.lymph_node, 'Mammary Gland': Organs.mammary_gland, 'Ovary': Organs.ovary, 'Pancreas': Organs.pancreas, 'Pituitary Gland': Organs.pituitary_gland, 'Spleen': Organs.spleen, 'Stomach': Organs.stomach, 'Testes': Organs.testes, 'Thymus': Organs.thymus, 'Thyroid Gland': Organs.thyroid_gland, 'Urinary Bladder': Organs.urinary_bladder, 'Uterus': Organs.uterus}¶

__doc__ = 'An enumeration.'¶

class ToxType(value)[source]¶

Bases: str, Enum

An enumeration.

chronic: str = 'chronic'¶

subchronic: str = 'subchronic'¶

multigenerational: str = 'multigenerational'¶

all: str = 'all'¶

_generate_next_value_(start, count, last_values)¶

Generate the next value when not given.

name: the name of the member start: the initial start value or None count: the number of existing members last_value: the last value assigned or None

__module__ = 'gt4sd.properties.molecules.core'¶

__annotations__ = {'all': <class 'str'>, 'chronic': <class 'str'>, 'multigenerational': <class 'str'>, 'subchronic': <class 'str'>}¶

__dict__ = mappingproxy({'_generate_next_value_': <function Enum._generate_next_value_>, '__module__': 'gt4sd.properties.molecules.core', '__annotations__': {'chronic': <class 'str'>, 'subchronic': <class 'str'>, 'multigenerational': <class 'str'>, 'all': <class 'str'>}, '__doc__': 'An enumeration.', '__dict__': <attribute '__dict__' of 'ToxType' objects>, '__weakref__': <attribute '__weakref__' of 'ToxType' objects>, '_member_names_': ['chronic', 'subchronic', 'multigenerational', 'all'], '_member_map_': {'chronic': <ToxType.chronic: 'chronic'>, 'subchronic': <ToxType.subchronic: 'subchronic'>, 'multigenerational': <ToxType.multigenerational: 'multigenerational'>, 'all': <ToxType.all: 'all'>}, '_member_type_': <class 'str'>, '_value2member_map_': {'chronic': <ToxType.chronic: 'chronic'>, 'subchronic': <ToxType.subchronic: 'subchronic'>, 'multigenerational': <ToxType.multigenerational: 'multigenerational'>, 'all': <ToxType.all: 'all'>}, 'chronic': <ToxType.chronic: 'chronic'>, 'subchronic': <ToxType.subchronic: 'subchronic'>, 'multigenerational': <ToxType.multigenerational: 'multigenerational'>, 'all': <ToxType.all: 'all'>, '__repr__': <function Enum.__repr__>, '__str__': <function Enum.__str__>, '__format__': <function Enum.__format__>, '__new__': <function Enum.__new__>})¶

__weakref__¶: list of weak references to the object (if defined)

_member_names_ = ['chronic', 'subchronic', 'multigenerational', 'all']¶

_member_map_ = {'all': ToxType.all, 'chronic': ToxType.chronic, 'multigenerational': ToxType.multigenerational, 'subchronic': ToxType.subchronic}¶

_member_type_¶: alias of str

_value2member_map_ = {'all': ToxType.all, 'chronic': ToxType.chronic, 'multigenerational': ToxType.multigenerational, 'subchronic': ToxType.subchronic}¶

__doc__ = 'An enumeration.'¶

algorithm_application: str¶

site: Organs¶

toxicity_type: ToxType¶

__dict__¶

__pydantic_fields_set__: set[str]¶: The names of fields explicitly set during instantiation.

__pydantic_extra__: dict[str, Any] | None¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_private__: dict[str, Any] | None¶: Values of private attributes set on the model instance.

__abstractmethods__ = frozenset({})¶

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': <class 'str'>, 'algorithm_name': 'str', 'algorithm_type': 'str', 'algorithm_version': 'str', 'domain': 'DomainSubmodule', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'site': <enum 'Organs'>, 'toxicity_type': <enum 'ToxType'>}¶

__class_vars__: ClassVar[set[str]] = {}¶: The names of the class variables defined on the model.

__doc__ = None¶

__module__ = 'gt4sd.properties.molecules.core'¶

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.molecules.core.OrganToxParameters'>, 'config': {'title': 'OrganToxParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.properties.molecules.core.OrganToxParameters'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.molecules.core.OrganToxParameters'>>]}, 'ref': 'gt4sd.properties.molecules.core.OrganToxParameters:94662814432336', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'OrganTox', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'MCA', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'domain': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': DomainSubmodule.molecules, 'schema': {'cls': <enum 'DomainSubmodule'>, 'members': [<DomainSubmodule.molecules: 'molecules'>, <DomainSubmodule.properties: 'properties'>, <DomainSubmodule.crystals: 'crystals'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.core.DomainSubmodule:94662808869760', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'site': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'Organs'>, 'members': [<Organs.adrenal_gland: 'Adrenal Gland'>, <Organs.bone_marrow: 'Bone Marrow'>, <Organs.brain: 'Brain'>, <Organs.eye: 'Eye'>, <Organs.heart: 'Heart'>, <Organs.kidney: 'Kidney'>, <Organs.liver: 'Liver'>, <Organs.lung: 'Lung'>, <Organs.lymph_node: 'Lymph Node'>, <Organs.mammary_gland: 'Mammary Gland'>, <Organs.ovary: 'Ovary'>, <Organs.pancreas: 'Pancreas'>, <Organs.pituitary_gland: 'Pituitary Gland'>, <Organs.spleen: 'Spleen'>, <Organs.stomach: 'Stomach'>, <Organs.testes: 'Testes'>, <Organs.thymus: 'Thymus'>, <Organs.thyroid_gland: 'Thyroid Gland'>, <Organs.urinary_bladder: 'Urinary Bladder'>, <Organs.uterus: 'Uterus'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.molecules.core.OrganToxParameters.Organs:94662814923712', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'toxicity_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': ToxType.all, 'schema': {'cls': <enum 'ToxType'>, 'members': [<ToxType.chronic: 'chronic'>, <ToxType.subchronic: 'subchronic'>, <ToxType.multigenerational: 'multigenerational'>, <ToxType.all: 'all'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.molecules.core.OrganToxParameters.ToxType:94662815069984', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OrganToxParameters', 'type': 'model-fields'}, 'type': 'model'}¶: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False¶: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶: The name of the post-init method for the model, if defined.

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x00005618676d7c50, ), serializer: Fields( GeneralFieldsSerializer { fields: { "domain": SerField { key_py: Py( 0x00007f1ea8dd1270, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dcea99230, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000561867189b80, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, }, "toxicity_type": SerField { key_py: Py( 0x00007f1dce91bab0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dcdb1a6c0, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000561867773720, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, }, "site": SerField { key_py: Py( 0x00007f1ea6ded8f0, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000056186774fbc0, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, "algorithm_type": SerField { key_py: Py( 0x00007f1ea52cf4f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dea152330, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_application": SerField { key_py: Py( 0x00007f1dcf4e0530, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dce919fb0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_version": SerField { key_py: Py( 0x00007f1ea52ed250, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "algorithm_name": SerField { key_py: Py( 0x00007f1ea52ce770, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dce8bc270, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "OrganToxParameters", }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OrganToxParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_key: Simple { key: "algorithm_type", py_key: Py( 0x00007f1dcadf2b30, ), path: LookupPath( [ S( "algorithm_type", Py( 0x00007f1dcadf2af0, ), ), ], ), }, name_py: Py( 0x00007f1ea52cf4f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dea152330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007f1dcadf2ab0, ), path: LookupPath( [ S( "domain", Py( 0x00007f1dcadf2a70, ), ), ], ), }, name_py: Py( 0x00007f1ea8dd1270, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dcea99230, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000561867189b80, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "crystals": 2, "molecules": 0, "properties": 1, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f1dcea99230, ), Py( 0x00007f1dcea992a0, ), Py( 0x00007f1dcea99310, ), ], }, missing: None, expected_repr: "'molecules', 'properties' or 'crystals'", strict: false, class_repr: "DomainSubmodule", name: "str-enum[DomainSubmodule]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[DomainSubmodule]]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key: Simple { key: "algorithm_name", py_key: Py( 0x00007f1dcadf2b70, ), path: LookupPath( [ S( "algorithm_name", Py( 0x00007f1dcadf2bb0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ce770, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dce8bc270, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key: Simple { key: "algorithm_version", py_key: Py( 0x00007f1dcadf5700, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f1dcadf57a0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ed250, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_key: Simple { key: "algorithm_application", py_key: Py( 0x00007f1dcadf5750, ), path: LookupPath( [ S( "algorithm_application", Py( 0x00007f1dcadf57f0, ), ), ], ), }, name_py: Py( 0x00007f1dcf4e0530, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dce919fb0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "site", lookup_key: Simple { key: "site", py_key: Py( 0x00007f1dcadf2bf0, ), path: LookupPath( [ S( "site", Py( 0x00007f1dcadf2c30, ), ), ], ), }, name_py: Py( 0x00007f1ea6ded8f0, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000056186774fbc0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "Uterus": 19, "Eye": 3, "Pituitary Gland": 12, "Spleen": 13, "Thymus": 16, "Heart": 4, "Urinary Bladder": 18, "Mammary Gland": 9, "Bone Marrow": 1, "Ovary": 10, "Adrenal Gland": 0, "Kidney": 5, "Stomach": 14, "Pancreas": 11, "Brain": 2, "Lymph Node": 8, "Lung": 7, "Liver": 6, "Thyroid Gland": 17, "Testes": 15, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f1dcdb19700, ), Py( 0x00007f1dcdb19770, ), Py( 0x00007f1dcdb199a0, ), Py( 0x00007f1dcdb19c40, ), Py( 0x00007f1dcdb19d20, ), Py( 0x00007f1dcdb19e70, ), Py( 0x00007f1dcdb19f50, ), Py( 0x00007f1dcdb19fc0, ), Py( 0x00007f1dcdb1a030, ), Py( 0x00007f1dcdb1a0a0, ), Py( 0x00007f1dcdb1a110, ), Py( 0x00007f1dcdb1a180, ), Py( 0x00007f1dcdb1a1f0, ), Py( 0x00007f1dcdb1a260, ), Py( 0x00007f1dcdb1a2d0, ), Py( 0x00007f1dcdb1a340, ), Py( 0x00007f1dcdb1a3b0, ), Py( 0x00007f1dcdb1a420, ), Py( 0x00007f1dcdb1a490, ), Py( 0x00007f1dcdb1a500, ), ], }, missing: None, expected_repr: "'Adrenal Gland', 'Bone Marrow', 'Brain', 'Eye', 'Heart', 'Kidney', 'Liver', 'Lung', 'Lymph Node', 'Mammary Gland', 'Ovary', 'Pancreas', 'Pituitary Gland', 'Spleen', 'Stomach', 'Testes', 'Thymus', 'Thyroid Gland', 'Urinary Bladder' or 'Uterus'", strict: false, class_repr: "OrganToxParameters.Organs", name: "str-enum[OrganToxParameters.Organs]", }, ), frozen: false, }, Field { name: "toxicity_type", lookup_key: Simple { key: "toxicity_type", py_key: Py( 0x00007f1dcadf2c70, ), path: LookupPath( [ S( "toxicity_type", Py( 0x00007f1dcadf2cb0, ), ), ], ), }, name_py: Py( 0x00007f1dce91bab0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dcdb1a6c0, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000561867773720, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "chronic": 0, "multigenerational": 2, "subchronic": 1, "all": 3, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f1dcdb1a570, ), Py( 0x00007f1dcdb1a5e0, ), Py( 0x00007f1dcdb1a650, ), Py( 0x00007f1dcdb1a6c0, ), ], }, missing: None, expected_repr: "'chronic', 'subchronic', 'multigenerational' or 'all'", strict: false, class_repr: "OrganToxParameters.ToxType", name: "str-enum[OrganToxParameters.ToxType]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[OrganToxParameters.ToxType]]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, ], model_name: "OrganToxParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x00005618676d7c50, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f1ea71db950, ), name: "OrganToxParameters", }, ), definitions=[], cache_strings=True)¶: The pydantic-core SchemaValidator used to validate instances of the model.

__signature__: ClassVar[Signature] = <Signature (*, algorithm_type: str = 'prediction', domain: gt4sd.properties.core.DomainSubmodule = <DomainSubmodule.molecules: 'molecules'>, algorithm_name: str = 'MCA', algorithm_version: str, algorithm_application: str = 'OrganTox', site: gt4sd.properties.molecules.core.OrganToxParameters.Organs, toxicity_type: gt4sd.properties.molecules.core.OrganToxParameters.ToxType = <ToxType.all: 'all'>) -> None>¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

_abc_impl = <_abc._abc_data object>¶

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'algorithm_application': FieldInfo(annotation=str, required=False, default='OrganTox'), 'algorithm_name': FieldInfo(annotation=str, required=False, default='MCA'), 'algorithm_type': FieldInfo(annotation=str, required=False, default='prediction'), 'algorithm_version': FieldInfo(annotation=str, required=True, description='Version of the algorithm', examples=['v0']), 'domain': FieldInfo(annotation=DomainSubmodule, required=False, default=<DomainSubmodule.molecules: 'molecules'>), 'site': FieldInfo(annotation=OrganToxParameters.Organs, required=True, description='name of the target site of interest.', examples=[<Organs.kidney: 'Kidney'>]), 'toxicity_type': FieldInfo(annotation=OrganToxParameters.ToxType, required=False, default=<ToxType.all: 'all'>, description='type of toxicity for which predictions are made.', examples=[<ToxType.chronic: 'chronic'>])}¶

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

class MolformerClassification(parameters)[source]¶

Bases: _Molformer

Class for all Molformer classification algorithms.

get_model(resources_path)[source]¶

Instantiate the actual model.

Parameters: resources_path (str) – local path to model files.
Returns: the model.
Return type: Predictor

__abstractmethods__ = frozenset({})¶

__annotations__ = {'max_runtime': 'int'}¶

__doc__ = 'Class for all Molformer classification algorithms.'¶

__module__ = 'gt4sd.properties.molecules.core'¶

__parameters__ = ()¶

_abc_impl = <_abc._abc_data object>¶

class MolformerMultitaskClassification(parameters)[source]¶

Bases: _Molformer

Class for all Molformer multitask classification algorithms.

get_model(resources_path)[source]¶

Instantiate the actual model.

Parameters: resources_path (str) – local path to model files.
Returns: the model.
Return type: Predictor

__abstractmethods__ = frozenset({})¶

__annotations__ = {'max_runtime': 'int'}¶

__doc__ = 'Class for all Molformer multitask classification algorithms.'¶

__module__ = 'gt4sd.properties.molecules.core'¶

__parameters__ = ()¶

_abc_impl = <_abc._abc_data object>¶

class MolformerRegression(parameters)[source]¶

Bases: _Molformer

Class for all Molformer regression algorithms.

get_model(resources_path)[source]¶

Instantiate the actual model.

Parameters: resources_path (str) – local path to model files.
Returns: the model.
Return type: Predictor

__abstractmethods__ = frozenset({})¶

__annotations__ = {'max_runtime': 'int'}¶

__doc__ = 'Class for all Molformer regression algorithms.'¶

__module__ = 'gt4sd.properties.molecules.core'¶

__parameters__ = ()¶

_abc_impl = <_abc._abc_data object>¶

class Plogp(parameters=PropertyPredictorParameters())[source]¶

Bases: CallablePropertyPredictor

Calculate the penalized logP of a molecule. This is the logP minus the number of rings with > 6 atoms minus the SAS.

__init__(parameters=PropertyPredictorParameters())[source]¶: Construct a PropertyPredictor using the related parameters. :type parameters: PropertyPredictorParameters :param parameters: parameters to configure the predictor.

__annotations__ = {}¶

__doc__ = 'Calculate the penalized logP of a molecule. This is the logP minus the number of\n rings with > 6 atoms minus the SAS.\n '¶

__module__ = 'gt4sd.properties.molecules.core'¶

class Lipinski(parameters=PropertyPredictorParameters())[source]¶

Bases: CallablePropertyPredictor

Calculate whether a molecule adheres to the Lipinski-rule-of-5. A crude approximation of druglikeness.

__init__(parameters=PropertyPredictorParameters())[source]¶: Construct a PropertyPredictor using the related parameters. :type parameters: PropertyPredictorParameters :param parameters: parameters to configure the predictor.

__annotations__ = {}¶

__doc__ = 'Calculate whether a molecule adheres to the Lipinski-rule-of-5.\n A crude approximation of druglikeness.\n '¶

__module__ = 'gt4sd.properties.molecules.core'¶

class Esol(parameters=PropertyPredictorParameters())[source]¶

Bases: CallablePropertyPredictor

Estimate the water solubility of a molecule.

__init__(parameters=PropertyPredictorParameters())[source]¶: Construct a PropertyPredictor using the related parameters. :type parameters: PropertyPredictorParameters :param parameters: parameters to configure the predictor.

__annotations__ = {}¶

__doc__ = 'Estimate the water solubility of a molecule.'¶

__module__ = 'gt4sd.properties.molecules.core'¶

class Scscore(parameters=ScscoreConfiguration(score_scale=5, fp_len=1024, fp_rad=2))[source]¶

Bases: CallablePropertyPredictor

Calculate the synthetic complexity score (SCScore) of a molecule.

__init__(parameters=ScscoreConfiguration(score_scale=5, fp_len=1024, fp_rad=2))[source]¶: Construct a PropertyPredictor using the related parameters. :type parameters: ScscoreConfiguration :param parameters: parameters to configure the predictor.

__annotations__ = {}¶

__doc__ = 'Calculate the synthetic complexity score (SCScore) of a molecule.'¶

__module__ = 'gt4sd.properties.molecules.core'¶

class Sas(parameters=PropertyPredictorParameters())[source]¶

Bases: CallablePropertyPredictor

Calculate the synthetic accessibility score (SAS) for a molecule.

__init__(parameters=PropertyPredictorParameters())[source]¶: Construct a PropertyPredictor using the related parameters. :type parameters: PropertyPredictorParameters :param parameters: parameters to configure the predictor.

__annotations__ = {}¶

__doc__ = 'Calculate the synthetic accessibility score (SAS) for a molecule.'¶

__module__ = 'gt4sd.properties.molecules.core'¶

class Bertz(parameters=PropertyPredictorParameters())[source]¶

Bases: CallablePropertyPredictor

Calculate Bertz index of a molecule.

__init__(parameters=PropertyPredictorParameters())[source]¶: Construct a PropertyPredictor using the related parameters. :type parameters: PropertyPredictorParameters :param parameters: parameters to configure the predictor.

__annotations__ = {}¶

__doc__ = 'Calculate Bertz index of a molecule.'¶

__module__ = 'gt4sd.properties.molecules.core'¶

class Tpsa(parameters=PropertyPredictorParameters())[source]¶

Bases: CallablePropertyPredictor

Calculate the total polar surface area of a molecule.

__init__(parameters=PropertyPredictorParameters())[source]¶: Construct a PropertyPredictor using the related parameters. :type parameters: PropertyPredictorParameters :param parameters: parameters to configure the predictor.

__annotations__ = {}¶

__doc__ = 'Calculate the total polar surface area of a molecule.'¶

__module__ = 'gt4sd.properties.molecules.core'¶

class Logp(parameters=PropertyPredictorParameters())[source]¶

Bases: CallablePropertyPredictor

Calculates the partition coefficient of a molecule.

__init__(parameters=PropertyPredictorParameters())[source]¶: Construct a PropertyPredictor using the related parameters. :type parameters: PropertyPredictorParameters :param parameters: parameters to configure the predictor.

__annotations__ = {}¶

__doc__ = 'Calculates the partition coefficient of a molecule.'¶

__module__ = 'gt4sd.properties.molecules.core'¶

class Qed(parameters=PropertyPredictorParameters())[source]¶

Bases: CallablePropertyPredictor

Calculate the quantitative estimate of drug-likeness (QED) of a molecule.

__init__(parameters=PropertyPredictorParameters())[source]¶: Construct a PropertyPredictor using the related parameters. :type parameters: PropertyPredictorParameters :param parameters: parameters to configure the predictor.

__annotations__ = {}¶

__doc__ = 'Calculate the quantitative estimate of drug-likeness (QED) of a molecule.'¶

__module__ = 'gt4sd.properties.molecules.core'¶

class NumberHAcceptors(parameters=PropertyPredictorParameters())[source]¶

Bases: CallablePropertyPredictor

Calculate number of H acceptors of a molecule.

__init__(parameters=PropertyPredictorParameters())[source]¶: Construct a PropertyPredictor using the related parameters. :type parameters: PropertyPredictorParameters :param parameters: parameters to configure the predictor.

__annotations__ = {}¶

__doc__ = 'Calculate number of H acceptors of a molecule.'¶

__module__ = 'gt4sd.properties.molecules.core'¶

class NumberAtoms(parameters=PropertyPredictorParameters())[source]¶

Bases: CallablePropertyPredictor

Calculate number of atoms of a molecule.

__init__(parameters=PropertyPredictorParameters())[source]¶: Construct a PropertyPredictor using the related parameters. :type parameters: PropertyPredictorParameters :param parameters: parameters to configure the predictor.

__annotations__ = {}¶

__doc__ = 'Calculate number of atoms of a molecule.'¶

__module__ = 'gt4sd.properties.molecules.core'¶

class NumberHDonors(parameters=PropertyPredictorParameters())[source]¶

Bases: CallablePropertyPredictor

Calculate number of H donors of a molecule.

__init__(parameters=PropertyPredictorParameters())[source]¶: Construct a PropertyPredictor using the related parameters. :type parameters: PropertyPredictorParameters :param parameters: parameters to configure the predictor.

__annotations__ = {}¶

__doc__ = 'Calculate number of H donors of a molecule.'¶

__module__ = 'gt4sd.properties.molecules.core'¶

class NumberAromaticRings(parameters=PropertyPredictorParameters())[source]¶

Bases: CallablePropertyPredictor

Calculate number of aromatic rings of a molecule.

__init__(parameters=PropertyPredictorParameters())[source]¶: Construct a PropertyPredictor using the related parameters. :type parameters: PropertyPredictorParameters :param parameters: parameters to configure the predictor.

__annotations__ = {}¶

__doc__ = 'Calculate number of aromatic rings of a molecule.'¶

__module__ = 'gt4sd.properties.molecules.core'¶

class NumberRings(parameters=PropertyPredictorParameters())[source]¶

Bases: CallablePropertyPredictor

Calculate number of rings of a molecule.

__init__(parameters=PropertyPredictorParameters())[source]¶: Construct a PropertyPredictor using the related parameters. :type parameters: PropertyPredictorParameters :param parameters: parameters to configure the predictor.

__annotations__ = {}¶

__doc__ = 'Calculate number of rings of a molecule.'¶

__module__ = 'gt4sd.properties.molecules.core'¶

class NumberRotatableBonds(parameters=PropertyPredictorParameters())[source]¶

Bases: CallablePropertyPredictor

Calculate number of rotatable bonds of a molecule.

__init__(parameters=PropertyPredictorParameters())[source]¶: Construct a PropertyPredictor using the related parameters. :type parameters: PropertyPredictorParameters :param parameters: parameters to configure the predictor.

__annotations__ = {}¶

__doc__ = 'Calculate number of rotatable bonds of a molecule.'¶

__module__ = 'gt4sd.properties.molecules.core'¶

class NumberLargeRings(parameters=PropertyPredictorParameters())[source]¶

Bases: CallablePropertyPredictor

Calculate the amount of large rings (> 6 atoms) of a molecule.

__init__(parameters=PropertyPredictorParameters())[source]¶: Construct a PropertyPredictor using the related parameters. :type parameters: PropertyPredictorParameters :param parameters: parameters to configure the predictor.

__annotations__ = {}¶

__doc__ = 'Calculate the amount of large rings (> 6 atoms) of a molecule.'¶

__module__ = 'gt4sd.properties.molecules.core'¶

class MolecularWeight(parameters=PropertyPredictorParameters())[source]¶

Bases: CallablePropertyPredictor

Calculate molecular weight of a molecule.

__init__(parameters=PropertyPredictorParameters())[source]¶: Construct a PropertyPredictor using the related parameters. :type parameters: PropertyPredictorParameters :param parameters: parameters to configure the predictor.

__annotations__ = {}¶

__doc__ = 'Calculate molecular weight of a molecule.'¶

__module__ = 'gt4sd.properties.molecules.core'¶

class IsScaffold(parameters=PropertyPredictorParameters())[source]¶

Bases: CallablePropertyPredictor

Whether a molecule is identical to its Murcko Scaffold.

__init__(parameters=PropertyPredictorParameters())[source]¶: Construct a PropertyPredictor using the related parameters. :type parameters: PropertyPredictorParameters :param parameters: parameters to configure the predictor.

__annotations__ = {}¶

__doc__ = 'Whether a molecule is identical to its Murcko Scaffold.'¶

__module__ = 'gt4sd.properties.molecules.core'¶

class NumberHeterocycles(parameters=PropertyPredictorParameters())[source]¶

Bases: CallablePropertyPredictor

The amount of heterocycles of a molecule.

__init__(parameters=PropertyPredictorParameters())[source]¶: Construct a PropertyPredictor using the related parameters. :type parameters: PropertyPredictorParameters :param parameters: parameters to configure the predictor.

__annotations__ = {}¶

__doc__ = 'The amount of heterocycles of a molecule.'¶

__module__ = 'gt4sd.properties.molecules.core'¶

class NumberStereocenters(parameters=PropertyPredictorParameters())[source]¶

Bases: CallablePropertyPredictor

The amount of stereo centers of a molecule.

__init__(parameters=PropertyPredictorParameters())[source]¶: Construct a PropertyPredictor using the related parameters. :type parameters: PropertyPredictorParameters :param parameters: parameters to configure the predictor.

__annotations__ = {}¶

__doc__ = 'The amount of stereo centers of a molecule.'¶

__module__ = 'gt4sd.properties.molecules.core'¶

class SimilaritySeed(parameters)[source]¶

Bases: CallablePropertyPredictor

Calculate the similarity of a molecule to a seed molecule.

__init__(parameters)[source]¶: Construct a PropertyPredictor using the related parameters. :type parameters: SimilaritySeedParameters :param parameters: parameters to configure the predictor.

__annotations__ = {}¶

__doc__ = 'Calculate the similarity of a molecule to a seed molecule.'¶

__module__ = 'gt4sd.properties.molecules.core'¶

class ActivityAgainstTarget(parameters)[source]¶

Bases: CallablePropertyPredictor

Calculate the activity of a molecule against a target molecule.

__init__(parameters)[source]¶: Construct a PropertyPredictor using the related parameters. :type parameters: ActivityAgainstTargetParameters :param parameters: parameters to configure the predictor.

__annotations__ = {}¶

__doc__ = 'Calculate the activity of a molecule against a target molecule.'¶

__module__ = 'gt4sd.properties.molecules.core'¶

class Askcos(parameters)[source]¶

Bases: ConfigurableCallablePropertyPredictor

A property predictor that uses the ASKCOs API to calculate the synthesizability of a molecule.

__init__(parameters)[source]¶: Construct a PropertyPredictor using the related parameters. :type parameters: AskcosParameters :param parameters: parameters to configure the predictor.

__annotations__ = {}¶

__doc__ = '\n A property predictor that uses the ASKCOs API to calculate the synthesizability\n of a molecule.\n '¶

__module__ = 'gt4sd.properties.molecules.core'¶

class MoleculeOne(parameters)[source]¶

Bases: CallablePropertyPredictor

A property predictor that uses the MoleculeOne API to calculate the synthesizability of a molecule.

__init__(parameters)[source]¶: Construct a PropertyPredictor using the related parameters. :type parameters: MoleculeOneParameters :param parameters: parameters to configure the predictor.

__annotations__ = {}¶

__doc__ = '\n A property predictor that uses the MoleculeOne API to calculate the synthesizability\n of a molecule.\n '¶

__module__ = 'gt4sd.properties.molecules.core'¶

class DockingTdc(parameters)[source]¶

Bases: ConfigurableCallablePropertyPredictor

A property predictor that computes the docking score against a target provided via the TDC package (see: https://tdcommons.ai/functions/oracles/#docking-scores)

__init__(parameters)[source]¶: Construct a PropertyPredictor using the related parameters. :type parameters: DockingTdcParameters :param parameters: parameters to configure the predictor.

__annotations__ = {}¶

__doc__ = '\n A property predictor that computes the docking score against a target\n provided via the TDC package (see: https://tdcommons.ai/functions/oracles/#docking-scores)\n '¶

__module__ = 'gt4sd.properties.molecules.core'¶

class Docking(parameters)[source]¶

Bases: ConfigurableCallablePropertyPredictor

A property predictor that computes the docking score against a user-defined target. Relies on TDC backend, see https://tdcommons.ai/functions/oracles/#docking-scores for setup.

__init__(parameters)[source]¶: Construct a PropertyPredictor using the related parameters. :type parameters: DockingParameters :param parameters: parameters to configure the predictor.

__annotations__ = {}¶

__doc__ = '\n A property predictor that computes the docking score against a user-defined target.\n Relies on TDC backend, see https://tdcommons.ai/functions/oracles/#docking-scores for setup.\n '¶

__module__ = 'gt4sd.properties.molecules.core'¶

class Tox21(parameters)[source]¶

Bases: _MCA

Model to predict environmental toxicity for the 12 endpoints in Tox21.

get_model(resources_path)[source]¶

Instantiate the actual model.

Parameters: resources_path (str) – local path to model files.
Returns: the model.
Return type: Predictor

classmethod get_description()[source]¶

Return type: str

__abstractmethods__ = frozenset({})¶

__annotations__ = {'max_runtime': 'int'}¶

__doc__ = 'Model to predict environmental toxicity for the 12 endpoints in Tox21.'¶

__module__ = 'gt4sd.properties.molecules.core'¶

__parameters__ = ()¶

_abc_impl = <_abc._abc_data object>¶

class ClinTox(parameters)[source]¶

Bases: _MCA

Model to predict environmental toxicity for the 12 endpoints in Tox21.

get_model(resources_path)[source]¶

Instantiate the actual model.

Parameters: resources_path (str) – local path to model files.
Returns: the model.
Return type: Predictor

classmethod get_description()[source]¶

Return type: str

__abstractmethods__ = frozenset({})¶

__annotations__ = {'max_runtime': 'int'}¶

__doc__ = 'Model to predict environmental toxicity for the 12 endpoints in Tox21.'¶

__module__ = 'gt4sd.properties.molecules.core'¶

__parameters__ = ()¶

_abc_impl = <_abc._abc_data object>¶

class Sider(parameters)[source]¶

Bases: _MCA

get_model(resources_path)[source]¶

Instantiate the actual model.

Parameters: resources_path (str) – local path to model files.
Returns: the model.
Return type: Predictor

classmethod get_description()[source]¶

Return type: str

__abstractmethods__ = frozenset({})¶

__annotations__ = {'max_runtime': 'int'}¶

__doc__ = None¶

__module__ = 'gt4sd.properties.molecules.core'¶

__parameters__ = ()¶

_abc_impl = <_abc._abc_data object>¶

class OrganTox(parameters)[source]¶

Bases: _MCA

Model to predict toxicity for different organs.

__init__(parameters)[source]¶

Targeted or untargeted generation.

Parameters: configuration – application specific helper that allows to setup the generator.

get_model(resources_path)[source]¶

Instantiate the actual model.

Parameters: resources_path (str) – local path to model files.
Returns: the model.
Return type: Predictor

classmethod get_description()[source]¶

Return type: str

__abstractmethods__ = frozenset({})¶

__annotations__ = {'max_runtime': 'int'}¶

__doc__ = 'Model to predict toxicity for different organs.'¶

__module__ = 'gt4sd.properties.molecules.core'¶

__parameters__ = ()¶

_abc_impl = <_abc._abc_data object>¶