MolToSDFTransformer#

class skfp.preprocessing.MolToSDFTransformer(filepath: str = 'mols.sdf', kekulize: bool = True, force_V3000: bool = False)#

Creates SDF file from RDKit Mol objects.

SDF (structure-data format) is processed for whole files, rather than individual molecules. For this reason .transform() saves the results directly to file.

If conf_id integer property is set for molecules, they are used to determine the conformer to save.

For details see RDKit documentation [1].

Parameters:

  • filepath (string, default="mols.sdf") – A string with file path location to save the SDF file. Tt should be a valid file path with .sdf extention.

  • kekulize (bool, default=True) – Whether to kekulize molecules before writing them to SDF file.

  • force_V3000 (bool, default=False) – Whether to force the V3000 format when writing to SDF file.

References

Examples

>>> from skfp.preprocessing import MolFromSDFTransformer, MolToSDFTransformer
>>> sdf_file_path = "mols_in.sdf"
>>> mol_from_sdf = MolFromSDFTransformer()
>>> mol_to_sdf = MolToSDFTransformer(filepath="mols_out.sdf")
>>> mol_to_sdf
MolToSDFTransformer(filepath='mols_out.sdf')

>>> mols = mol_from_sdf.transform(sdf_file_path)  
>>> mol_to_sdf.transform(mols)

Methods

fit(X[, y])

Unused, kept for Scikit-learn compatibility.

fit_transform(X[, y])

The same as .transform() method, kept for Scikit-learn compatibility.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

set_output(*[, transform])

Set output container.

set_params(**params)

Set the parameters of this estimator.

set_transform_request(*[, copy])

Request metadata passed to the transform method.

transform(X[, copy])

fit(X, y=None, **fit_params)#

Unused, kept for Scikit-learn compatibility.

Parameters:

  • X (any) – Unused, kept for Scikit-learn compatibility.

  • y (any) – Unused, kept for Scikit-learn compatibility.

  • **fit_params (dict) – Unused, kept for Scikit-learn compatibility.

Return type:

self

fit_transform(X, y=None, **fit_params)#

The same as .transform() method, kept for Scikit-learn compatibility.

Parameters:

  • X (any) – See .transform() method.

  • y (any) – See .transform() method.

  • **fit_params (dict) – Unused, kept for Scikit-learn compatibility.

Returns:

X_new – See .transform() method.

Return type:

any

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routing – A MetadataRequest encapsulating routing information.

Return type:

MetadataRequest

get_params(deep=True)#

Get parameters for this estimator.

Parameters:

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params – Parameter names mapped to their values.

Return type:

dict

set_output(*, transform=None)#

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform ({"default", "pandas", "polars"}, default=None) –

Configure output of transform and fit_transform.

  • ”default”: Default output format of a transformer

  • ”pandas”: DataFrame output

  • ”polars”: Polars output

  • None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

Returns:

self – Estimator instance.

Return type:

estimator instance

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**params (dict) – Estimator parameters.

Returns:

self – Estimator instance.

Return type:

estimator instance

set_transform_request(*, copy: bool | None | str = '$UNCHANGED$') → MolToSDFTransformer#

Request metadata passed to the transform method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to transform if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to transform.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

copy (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for copy parameter in transform.

Returns:

self – The updated object.

Return type:

object