MolToSmilesTransformer#

class skfp.preprocessing.MolToSmilesTransformer(isomeric_smiles: bool = True, kekule_smiles: bool = False, canonical: bool = True, all_bonds_explicit: bool = False, all_hs_explicit: bool = False, do_random: bool = False, n_jobs: int | None = None, batch_size: int | None = None, suppress_warnings: bool = False, verbose: int | dict = 0)#

Creates SMILES strings from RDKit Mol objects.

For details see RDKit documentation [1].

Parameters:

isomeric_smiles (bool, default=True) – Whether to include information about stereochemistry.
kekule_smiles (bool, default=False) – Whether to use the Kekule form (no aromatic bonds).
canonical (bool, default=True) – Whether to canonicalize the molecule. This results in a reproducible SMILES, given the same input molecule (if do_random is not used).
all_bonds_explicit (bool, default=False) – Whether to explicitly indicate all bond orders.
all_hs_explicit (bool, default=False) – Whether to explicitly indicate all hydrogens.
do_random (bool, default=False) – If True, randomizes the traversal of the molecule graph, generating random SMILES.
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.
suppress_warnings (bool, default=False) – Whether to suppress warnings and errors on saving molecules.
verbose (int or dict, default=0) – Controls the verbosity when processing molecules. If a dictionary is passed, it is treated as kwargs for tqdm(), and can be used to control the progress bar.

References

Examples

>>> from skfp.preprocessing import MolFromSmilesTransformer, MolToSmilesTransformer
>>> smiles = ["O", "CC", "[C-]#N", "CC=O"]
>>> mol_from_smiles = MolFromSmilesTransformer()
>>> mol_to_smiles = MolToSmilesTransformer()
>>> mol_to_smiles
MolToSmilesTransformer()

>>> mols = mol_from_smiles.transform(smiles)
>>> mol_to_smiles.transform(mols)
['O', 'CC', '[C-]#N', 'CC=O']

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_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])	Transform inputs.

fit(X, y=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, y=None, **fit_params)#

The same as .transform() method, kept for scikit-learn compatibility.

Parameters:

X (any) – See .transform() method.
y (any) – Unused, kept for scikit-learn compatibility.
**fit_params (dict) – Unused, kept for scikit-learn compatibility.

Returns:

X_new – See .transform() method.

Return type:

any

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 = '$UNCHANGED$') → MolToSmilesTransformer#

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)#

Transform inputs. Output type depends on the inheriting class, but should be a sequence with the same length as input.

Parameters:

X ({sequence, array-like} of shape (n_samples,)) – Sequence containing SMILES strings or RDKit Mol objects. Depending on the implementation in the inheriting class, it may require using Mol objects with computed conformations and with conf_id property set.
copy (bool, default=False) – Copy the input X or not.

Returns:

X – Transformed inputs.

Return type:

{sequence, array-like} of shape (n_samples, any)