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. It should be a valid file path with .sdf extension.
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])	Configure whether metadata should be requested to be passed to the `transform` method.
`transform`(X[, copy])	Write RDKit `Mol` objects to SDF file at location given by `filepath` parameter.

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) – Unused, kept for scikit-learn compatibility.
**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#

Configure whether metadata should be requested to be passed to the transform method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the 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.

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

transform(X: Sequence[Mol], copy: bool = False) → None#

Write RDKit Mol objects to SDF file at location given by filepath parameter. File is created if necessary, and overwritten if it exists already.

Parameters:

X ({sequence, array-like} of shape (n_samples,)) – Sequence containing RDKit Mol objects.
copy (bool, default=False) – Unused, kept for scikit-learn compatibility.

Return type:

None