RDFFingerprint#

class skfp.fingerprints.RDFFingerprint(sparse: bool = False, n_jobs: int | None = None, batch_size: int | None = None, verbose: int = 0)#

RDF (Radial Distribution Function descriptors) fingerprint.

The implementation uses RDKit. This is a descriptor-based fingerprint, where features are based on the radial distribution function (RDF) of interatomic distances.

RDF function can be interpreted as the probability distribution of finding an atom in a spherical volume of given radius r, and is defined for all atoms i and j with distance \(r_{ij}\) in the molecule with N atoms as:

\[RDF(r) = \sum_{i}^{N-1} \sum_{j > i}^N w_i * w_j * e^{B (r - r_{ij})^2}\]

This results in a Gaussian distribution, centered around each distance \(r_{ij}\), with width depending on the smoothing parameter B, which is set to 100 (similar to DRAGON software). Radii between 1 and 30 (inclusive) are used, corresponding to distances from 1Å to 16Å.

7 weighting variants are used, unweighted and 6 based on atomic features: unweighted, atomic mass, van der Waals volume, electronegativity, polarizability, ion polarity, and IState [1] [2]. They are relative to the carbon, e.g. molecular weight is: MW(atom_type) / MW(carbon).

This results in 210 features. They are invariant to translation and rotation, independent of molecule size, and unique for a given conformation. See [3] [4] [5] [6] for details.

Parameters:

  • 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, default=0) – Controls the verbosity when computing fingerprints.

n_features_out#

Number of output features, size of fingerprints.

Type:

int = 210

requires_conformers#

Value is always True, as this fingerprint is 3D based. It always requires molecules with conformers as inputs, with conf_id integer property set.

Type:

bool = True

See also

MORSEFingerprint

Related fingerprint, which uses scattered electron intensity instead of radial distribution function.

References

Examples

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

>>> mol_from_smiles = MolFromSmilesTransformer()
>>> mols = mol_from_smiles.transform(smiles)
>>> conf_gen = ConformerGenerator()
>>> mols = conf_gen.transform(mols)
>>> fp.transform(mols)  
array([[1.930e+00, 2.070e-01, 0.000e+00, ..., 0.000e+00, 0.000e+00, 0.000e+00],
       [1.790e+00, 9.990e-01, 4.160e-01, ..., 0.000e+00, 0.000e+00, 0.000e+00],
       [1.150e-01, 0.000e+00, 0.000e+00, ..., 0.000e+00, 0.000e+00, 0.000e+00],
       [1.427e+00, 9.920e-01, 1.443e+00, ..., 0.000e+00, 0.000e+00, 0.000e+00]])

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 output feature names for transformation.

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(input_features=None)#

Get output feature names for transformation.

The feature names out will prefixed by the lowercased class name. For example, if the transformer outputs 3 features, then the feature names out are: [“class_name0”, “class_name1”, “class_name2”].

Parameters:

input_features (array-like of str or None, default=None) – Only used to validate feature names with the names seen in fit.

Returns:

feature_names_out – Transformed 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 = '$UNCHANGED$') → RDFFingerprint#

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