BCUT2DFingerprint#

class skfp.fingerprints.BCUT2DFingerprint(partial_charge_model: str = 'formal', charge_errors: str = 'raise', errors: str = 'raise', n_jobs: int | None = None, batch_size: int | None = None, verbose: int | dict = 0)#

Burden-CAS-University of Texas (BCUT2D) fingerprint.

This is a descriptor-based fingerprint, based on Burden descriptors, which are largest and smallest eigenvalues of the Burden matrix [1] [2]. It is a modified connectivity matrix, aimed to combine topological structure with atomic properties. Diagonal elements are atom descriptors, e.g. atomic number, charge. Off-diagonal elements for bonded atoms are 1/sqrt(bond order), with minimum of 0.001 in case of no bond between given pair of atoms.

BCUT2D descriptors use the largest and smallest eigenvalue for 4 atomic properties: mass, charge, logP and molar refractivity (MR). This results in 8 features.

The implementation differs slightly from RDKit [3], because they use Gasteiger model by default, and here formal charge model is used. It is simpler and more robust, since Gasteiger fails for metal atoms. Like RDKit, we use Wildman-Crippen atomic contributions model [4] for logP and MR.

Parameters:

  • partial_charge_model ({"Gasteiger", "MMFF94", "formal", "precomputed"}, default="formal") – Which model to use to compute atomic partial charges. Default "formal" computes formal charges, and is the simplest and most error-resistant one. "precomputed" assumes that the inputs are RDKit PropertyMol objects with “charge” float property set.

  • charge_errors ({"raise", "ignore", "zero"}, default="raise") – How to handle errors during calculation of atomic partial charges. "raise" immediately raises any errors. "NaN" ignores any atoms that failed the computation; note that if all atoms fail, the error will be raised (use errors parameter to control this). "zero" uses default value of 0 to fill all problematic charges.

  • errors ({"raise", "NaN", "ignore"}, default="raise") – How to handle errors during fingerprint calculation. "raise" immediately raises any errors. "NaN" returns NaN values for molecules which resulted in errors. "ignore" suppresses errors and does not return anything for molecules with errors. This potentially results in fewer output vectors than input molecules, and should be used with caution.

  • 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.

  • 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 computing fingerprints. If a dictionary is passed, it is treated as kwargs for tqdm(), and can be used to control the progress bar.

n_features_out#

Number of output features, size of fingerprints.

Type:

int = 8

requires_conformers#

This fingerprint uses only 2D molecular graphs and does not require conformers.

Type:

bool = False

References

Examples

>>> from skfp.fingerprints import BCUT2DFingerprint
>>> smiles = ["O", "CC", "[C-]#N", "CC=O"]
>>> fp = BCUT2DFingerprint()
>>> fp
BCUT2DFingerprint()

>>> fp.transform(smiles)
array([[15.999     , 15.999     ,  0.        ,  0.        , -0.2893    ,
        -0.2893    ,  0.8238    ,  0.8238    ],
       [13.011     , 11.011     ,  1.        , -1.        ,  1.1441    ,
        -0.8559    ,  3.503     ,  1.503     ],
       [14.16196892, 11.85603108,  0.26376262, -1.26376262,  0.6264836 ,
        -0.5301136 ,  3.43763218,  1.53036782],
       [16.12814585, 10.96029404,  1.22521641, -1.2242736 ,  1.12869643,
        -1.35803037,  5.43954434, -0.10561046]])

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 fingerprint output feature 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])

Request metadata passed to the transform method.

transform(X[, copy])

Compute BCUT2D fingerprints.

transform_x_y(X, y[, copy])

Compute BCUT2D fingerprints.
fit(X: Sequence[str | Mol], y: Any | None = 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: Sequence[str | Mol], y: Any | 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 fingerprint output feature names. They are largest and smallest eigenvalues of Burden matrix for 4 atomic properties.

Parameters:

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

Returns:

feature_names_out – BCUT2D feature 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 = '$UNCHANGED$') → BCUT2DFingerprint#

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

transform(X: Sequence[str | Mol], copy: bool = False) → ndarray | csr_array#

Compute BCUT2D fingerprints.

Parameters:

  • X ({sequence, array-like} of shape (n_samples,)) – Sequence containing RDKit Mol objects, with conformers generated and conf_id integer property set.

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

Returns:

X – Array with fingerprints.

Return type:

{ndarray, sparse matrix} of shape (n_samples, 15)

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

Compute BCUT2D fingerprints. The returned values for X and y are properly synchronized.

Parameters:

  • X ({sequence, array-like} of shape (n_samples,)) – Sequence containing RDKit Mol objects, with conformers generated and conf_id integer property set.

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

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

Returns:

  • X ({ndarray, sparse matrix} of shape (n_samples, 15)) – Array with fingerprints.

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