AutocorrFingerprint

class skfp.fingerprints.AutocorrFingerprint(use_3D: bool = False, n_jobs: int | None = None, batch_size: int | None = None, verbose: int = 0)

Autocorrelation fingerprint.

The implementation uses RDKit. This is a descriptor-based fingerprint, where bits measure strength of autocorrelation of molecular properties between atoms with different shortest path distances. For 3D variant, those distances are further weighted by Euclidean distance between atoms in a given conformation.

It uses a hydrogen-depleted molecule, and for each heavy atom computes 6 features: atomic mass, van der Waals volume, electronegativity, polarizability, ion polarity, and IState [1] [2]. They are then made relative to the carbon, e.g. molecular weight is: MW(atom_type) / MW(carbon).

Four autocorrelation measures are used: Moreau-Broto, centered (average) Moreau-Broto, Moran and Geary [3] [4]. They are calculated using topological distances (shortest paths), with distance between 1 and 8 (inclusive). This results in 192 features: 6 atom features * 4 autocorrelations * 8 distances.

3D variant has the following differences:

• requires passing molecules with conformers and conf_id integer property set

• weights topological distances by Euclidean distance between atoms

• uses 2 additional features: constant 1 (which measures Euclidean distance autocorrelation due to weighting) and covalent radius (RCov)

• uses shortest paths distances between 1 and 9 (inclusive)

• uses only Moreau-Broto autocrrelation

• results in 80 features: 8 atom features * 10 distances

For more details on autocorrelation descriptors, see [5].

Parameters:

• use_3D (bool, default=False) – Whether to use 3D Euclidean distance matrix. If False, only uses topological distances on molecular graph.

• 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. Equal to 192 for 2D and 80 for 3D, which depends on the use_3D parameter.

Type:

int = 192 or 80

requires_conformers

Whether the fingerprint is 3D-based and requires molecules with conformers as inputs, with conf_id integer property set. This depends on the use_3D parameter, and has the same value.

Type:

bool

References

[1]

RDKit IState discussion rdkit/rdkit#6122

[2]

RDKit IState implementation rdkit/rdkit

[3]

Rajarshi Guha “Autocorrelation Descriptors”

[4]

Roberto Todeschini and Viviana Consonni “Molecular Descriptors for Chemoinformatics”

[5]

Guillaume Godin “3D descriptors in RDKit” UGM 2017

Examples

>>> from skfp.fingerprints import AutocorrFingerprint
>>> smiles = ["CCO", "CCN"]
>>> fp = AutocorrFingerprint()
>>> fp
AutocorrFingerprint()

>>> fp.transform(smiles)  
array([[ 1.204,  0.847,  0.   ,  ...,  0.   ,  0.   ,  0.   ],
       [ 1.153,  0.773,  0.   ,  ...,  0.   ,  0.   ,  0.   ]])

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 = '$UNCHANGED$') → AutocorrFingerprint

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