RDKit2DDescriptorsFingerprint#

class skfp.fingerprints.RDKit2DDescriptorsFingerprint(normalized: bool = False, clip_val: float = 2147483647, sparse: bool = False, n_jobs: int | None = None, batch_size: int | None = None, verbose: int | dict = 0)#

RDKit 2D descriptors fingerprint.

The implementation uses descriptastorus [1] and RDKit. This fingerprint consists of 200 2D descriptors available in RDKit (almost all). List of all features is available in descriptastorus code and in the supplementary material of the original paper[2]_.

Normalized variant uses cumulative distribution function (CDF) normalization, as proposed in [2]. Distributions for normalization have been determined using a large collection of molecules from ChEMBL [3].

Typical correct values should be small, but it often results in NaN or infinity for some descriptors. Value clipping with clip_val parameter, feature selection, and/or imputation should be used.

Parameters:

  • normalized (bool, default=False) – Whether to return CDF-normalized descriptor values.

  • clip_val (float or None, default=2147483647) – Value to clip results at, both positive and negative ones.The default value is the maximal value of 32-bit integer, but should often be set lower, depending on the application. None means that no clipping is applied.

  • sparse (bool, default=False) – Whether to return dense NumPy array, or sparse SciPy CSR array.

  • 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 = 200

requires_conformers#

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

Type:

bool = False

References

Examples

>>> from skfp.fingerprints import RDKit2DDescriptorsFingerprint
>>> smiles = ["O", "CC", "[C-]#N", "CC=O"]
>>> fp = RDKit2DDescriptorsFingerprint()
>>> fp
RDKit2DDescriptorsFingerprint()

>>> fp.transform(smiles)  
array([[ 1.00000000e+00,  0.00000000e+00,  ...  0.00000000e+00,  3.27747673e-01]
       [ 1.00000000e+00,  1.00000000e+00,  ...  0.00000000e+00,  3.72785568e-01]
       [ 1.00000000e+00,  3.00000000e+00,  ...  0.00000000e+00,  3.44374359e-01]
       [ 1.00000000e+00,  2.18749619e+00,  ...  0.00000000e+00,  3.55007619e-01]], dtype=float32)

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()

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])

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()#

Get fingerprint output feature names.

Returns:

feature_names_out – Names of the RDKit 2D descriptors.

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 = '$UNCHANGED$') → RDKit2DDescriptorsFingerprint#

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