MolStandardizer#

class skfp.preprocessing.MolStandardizer(largest_fragment_only: bool = False, n_jobs: int | None = None, verbose: int = 0)#

Performs common molecule standardization operations.

Applies the following cleanup transformations to the inputs: - create RDKit Mol objects, if SMILES strings are passed - sanitize [1] (performs basic validity checks) - if largest_fragment_only, select the largest fragment for further processing - remove hydrogens - disconnect metal atoms - normalize (transform functional groups to normal form) - reionize

See [1] [2] [3] [4] [5] for details, rationale and alternatives. Note that there is no one-size-fits-all solution, and here we use pretty minimalistic, most common steps. This class by design does not allow much parametrization, and for custom purposes you should build the pipeline yourself.

New molecules are always returned, and any set properties are not kept, as this should normally be the first step in a pipeline.

Parameters:

largest_fragment_only (bool, default=False) – Whether to select only the largest fragment from each molecule.
n_jobs (int, default=None) – The number of jobs to run in parallel. transform() is parallelized over the input molecules. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Scikit-learn documentation on n_jobs for more details.
verbose (int, default=0) – Controls the verbosity when standardizing molecules. By default, all warnings are turned off.

References

Examples

>>> from skfp.preprocessing import MolStandardizer
>>> smiles = ["O", "CC", "[C-]#N", "CC=O"]
>>> standardizer = MolStandardizer()
>>> standardizer
MolStandardizer()

>>> standardizer.transform(smiles)  
    [<rdkit.Chem.rdchem.Mol object at ...>,
     <rdkit.Chem.rdchem.Mol object at ...>,
     <rdkit.Chem.rdchem.Mol object at ...>,
     <rdkit.Chem.rdchem.Mol object at ...>]

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 = '$UNCHANGED$') → MolStandardizer#

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