RDFFingerprint#

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

Configure whether metadata should be requested to be passed to the transform method.

transform(X[, copy])

Compute RDF 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 correspond to 7 weighting variants and 30 radii.

Parameters:

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

Returns:

feature_names_out – RDF 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#

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) → ndarray | csr_array#

Compute RDF fingerprints.

Parameters:

  • X ({sequence of str or Mol}) – Sequence containing RDKit Mol objects, with conformers generated and conf_id integer property set.

  • copy (bool, default=False) – Whether to copy input data.

Returns:

X – Transformed data.

Return type:

{ndarray, sparse matrix} of shape (n_samples, 210)