TopologicalTorsionFingerprint#

class skfp.fingerprints.TopologicalTorsionFingerprint(fp_size: int = 2048, torsion_atom_count: int = 4, use_pharmacophoric_invariants: bool = False, include_chirality: bool = False, count_simulation: bool = True, count: bool = False, sparse: bool = False, n_jobs: int | None = None, batch_size: int | None = None, verbose: int | dict = 0)#

Topological Torsion fingerprint.

The implementation uses RDKit. This is a hashed fingerprint, where the hashed fragments are computed based on topological torsions [1].

A topological torsion is defined as a linear sequence of consecutively bonded heavy (non-hydrogen): (atom 1 type)-(atom 2 type)-(atom 3 type)-(atom 4 type)

Atom type takes into consideration:

  • atomic number

  • number of pi electrons

  • degree (number of bonds)

This example of 4 atom path is the canonical version of topological torsion. The number of atoms can be adjusted (using torsion_atom_count parameter).

Parameters:

  • fp_size (int, default=2048) – Size of output vectors, i.e. number of bits for each fingerprint. Must be positive.

  • torsion_atom_count (int, default=4) – The number of atoms to be included in the torsion.

  • use_pharmacophoric_invariants (bool, default=False) – Whether to use pharmacophoric invariants (atom types) instead of default ones. They are the same as in the FCFP fingerprint: Donor, Acceptor, Aromatic, Halogen, Basic, Acidic.

  • include_chirality (bool, default=False) – Whether to include chirality information when computing atom types.

  • count_simulation (bool, default=True) – Whether to use count simulation for approximating feature counts [2].

  • count (bool, default=False) – Whether to return binary (bit) features, or their counts.

  • 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. Equal to fp_size.

Type:

int

requires_conformers#

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

Type:

bool = False

See also

AtomPairFingerprint

Related fingerprint, but uses 2 atoms and the distance between them.

References

Examples

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

>>> fp.transform(smiles)
array([[0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0]], shape=(4, 2048), dtype=uint8)

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])

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

transform(X[, copy])

Compute Topological Torsion 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)#

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 = '$UNCHANGED$') → TopologicalTorsionFingerprint#

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 Topological Torsion fingerprints.

Parameters:

  • X ({sequence, array-like} of shape (n_samples,)) – Sequence containing SMILES strings or RDKit Mol objects.

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

Returns:

X – Array with fingerprints.

Return type:

{ndarray, sparse matrix} of shape (n_samples, self.fp_size)