BrenkFilter#

class skfp.filters.BrenkFilter(allow_one_violation: bool = False, return_type: str = 'mol', n_jobs: int | None = None, batch_size: int | None = None, verbose: int | dict = 0)#

Brenk filter.

Designed to filter out molecules containing substructures with undesirable pharmacokinetics or toxicity, e.g. sulfates, phosphates, nitro groups. Resulting set should be reasonable lead-like molecules for optimization campaigns and HTS.

Rule definitions are available in the supplementary material of the original publication [1] and in RDKit code [2].

Parameters:

  • allow_one_violation (bool, default=False) – Whether to allow violating one of the rules for a molecule. This makes the filter less restrictive.

  • return_type ({"mol", "indicators", "condition_indicators"}, default="mol") –

    What values to return as the filtering result.

    • "mol" - return a list of molecules remaining in the dataset after filtering

    • "indicators" - return a binary vector with indicators which molecules pass the filter (1) and which would be removed (0)

    • "condition_indicators" - return a Pandas DataFrame with molecules in rows, filter conditions in columns, and 0/1 indicators whether a given condition was fulfilled by a given molecule

  • n_jobs (int, default=None) – The number of jobs to run in parallel. transform_x_y() and transform() are 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.

  • batch_size (int, default=None) – Number of inputs processed in each batch. None divides input data into equal-sized parts, as many as n_jobs.

  • verbose (int or dict, default=0) – Controls the verbosity when filtering molecules. If a dictionary is passed, it is treated as kwargs for tqdm(), and can be used to control the progress bar.

References

Examples

>>> from skfp.filters import BrenkFilter
>>> smiles = ["C", "CN1C=NC2=C1C(=O)N(C(=O)N2C)C", "c1cc([NH2])ccc1"]
>>> filt = BrenkFilter()
>>> filt
BrenkFilter()

>>> filtered_mols = filt.transform(smiles)
>>> filtered_mols
['C', 'CN1C=NC2=C1C(=O)N(C(=O)N2C)C']

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_feature_names_out([input_features])

Get filter condition names.

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])

Apply a filter to input molecules.

transform_x_y(X, y[, copy])

Apply a filter to input molecules.
fit(X: Sequence[str | Mol], y: ndarray | None = None)#

Unused, kept for scikit-learn compatibility.

Parameters:

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

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

Return type:

self

fit_transform(X: Sequence[str | Mol], y: ndarray | None = 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_feature_names_out(input_features=None) → ndarray#

Get filter condition names. They correspond to molecular descriptors (for physicochemical filters) or SMARTS patterns (for substructural filters).

Parameters:

input_features (array-like of str or None, default=None) – Unused, kept for scikit-learn compatibility.

Returns:

feature_names_out – Filter condition names.

Return type:

ndarray of str objects

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 = '$UNCHANGED$') → BrenkFilter#

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[str | Mol], copy: bool = False) → list[str | Mol] | ndarray#

Apply a filter to input molecules. Output depends on return_type attribute.

Parameters:

  • X ({sequence of str or Mol}) – Sequence containing SMILES strings or RDKit Mol objects.

  • copy (bool, default=False) – Copy the input X or not.

Returns:

X – or array of shape (n_samples, n_conditions) List with filtered molecules or indicators.

Return type:

list of shape (n_samples,) or array of shape (n_samples,)

transform_x_y(X: Sequence[str | Mol], y: ndarray, copy: bool = False) → tuple[list[str | Mol], ndarray] | tuple[ndarray, ndarray]#

Apply a filter to input molecules. Output depends on return_type attribute.

Parameters:

  • X ({sequence of str or Mol}) – Sequence containing SMILES strings or RDKit Mol objects.

  • y (array-like of shape (n_samples,)) – Array with labels for molecules.

  • copy (bool, default=False) – Copy the input X or not.

Returns:

  • X (list of shape (n_samples,) or array of shape (n_samples,)) – or array of shape (n_samples, n_conditions) List with filtered molecules or indicators.

  • y (np.ndarray of shape (n_samples,)) – Array with labels for molecules.