`RepEngineSkfp` — scikit-fingerprints Engine¶

Module: autopeptideml.reps.fps
Inherits from: RepEngineBase

Overview¶

RepEngineSkfp wraps any scikit-fingerprints (skfp) fingerprint class as a drop-in RepEngineBase-compatible engine. It gives access to 28 fingerprint families beyond the RDKit-backed ones available in RepEngineFP, including MACCS, AtomPair, TopologicalTorsion, Avalon, PubChem, Mordred, and more.

Requires: pip install scikit-fingerprints
(RDKit is also required as a transitive dependency.)

Attributes¶

Attribute	Type	Description
`engine`	`str`	Fixed to `'skfp'`.
`name`	`str`	Auto-generated as `'skfp-<rep>'`, e.g. `'skfp-maccs'`.
`generator`	`BaseFingerprintTransformer`	The underlying skfp transformer instance.

Constructor¶

RepEngineSkfp(rep: str, **kwargs)

Parameter	Type	Description
`rep`	`str`	Lowercase fingerprint key (see Supported fingerprints).
`**kwargs`	`Any`	Forwarded verbatim to the skfp fingerprint constructor (e.g. `fp_size`, `radius`, `count`).

Methods¶

`compute_reps` (inherited)¶

compute_reps(
    mols: List[str],
    verbose: bool = False,
    batch_size: int = 12
) -> np.ndarray

Compute fingerprints for a list of SMILES strings. Returns an array of shape (n_mols, dim).

`dim`¶

dim() -> int

Returns generator.n_features_out — the feature dimensionality reported by the underlying skfp transformer.

`_preprocess_batch`¶

_preprocess_batch(batch: List[str]) -> List[str]

Returns the batch unchanged. skfp transformers accept SMILES strings directly and handle Mol conversion internally.

`_rep_batch`¶

_rep_batch(batch: List[str]) -> np.ndarray

Delegates to generator.transform(batch). Returns a dense np.ndarray of shape (len(batch), dim).

`_load_generator`¶

_load_generator(rep: str, **kwargs) -> BaseFingerprintTransformer

Looks up rep in the internal class map and instantiates the matching skfp class with **kwargs. Raises NotImplementedError for unknown keys.

Supported fingerprints¶

Key	skfp class	Fixed `dim`	Notes
`atompair`	`AtomPairFingerprint`	`fp_size`	Hashed atom-pair counts
`autocorr`	`AutocorrFingerprint`	192	2D autocorrelation descriptors
`avalon`	`AvalonFingerprint`	`fp_size`	Avalon substructure fingerprint
`bcut2d`	`BCUT2DFingerprint`	64	BCUT2D descriptors
`ecfp`	`ECFPFingerprint`	`fp_size`	Extended connectivity (Morgan); pass `use_pharmacophoric_invariants=True` for FCFP
`erg`	`ERGFingerprint`	315	Extended reduced graph
`estate`	`EStateFingerprint`	79	Electrotopological state
`functionalgroups`	`FunctionalGroupsFingerprint`	85	Functional group presence
`ghosecrippen`	`GhoseCrippenFingerprint`	110	Ghose-Crippen atom types
`klekotaroth`	`KlekotaRothFingerprint`	`fp_size`	Klekota-Roth substructure
`laggner`	`LaggnerFingerprint`	307	Laggner substructure
`layered`	`LayeredFingerprint`	`fp_size`	RDKit layered fingerprint
`lingo`	`LingoFingerprint`	`fp_size`	SMILES n-gram similarity
`maccs`	`MACCSFingerprint`	166	MACCS structural keys
`map`	`MAPFingerprint`	`fp_size`	MinHashed atom-pair
`mhfp`	`MHFPFingerprint`	`fp_size`	MinHashed fingerprint
`mordred`	`MordredFingerprint`	1613	Mordred 2D descriptors
`mqns`	`MQNsFingerprint`	42	Molecular quantum numbers
`pattern`	`PatternFingerprint`	`fp_size`	RDKit pattern fingerprint
`pharmacophore`	`PharmacophoreFingerprint`	`fp_size`	2D pharmacophore
`pubchem`	`PubChemFingerprint`	881	PubChem substructure keys
`rdkit`	`RDKitFingerprint`	`fp_size`	RDKit path fingerprint
`rdkit2d`	`RDKit2DDescriptorsFingerprint`	200	RDKit 2D descriptors
`secfp`	`SECFPFingerprint`	`fp_size`	SMILES extended connectivity
`topologicaltorsion`	`TopologicalTorsionFingerprint`	`fp_size`	Topological torsion
`usr`	`USRFingerprint`	12	Ultrafast shape recognition (3D)
`usrcat`	`USRCATFingerprint`	60	USR + CREDO atom types (3D)
`vsa`	`VSAFingerprint`	71	Van der Waals surface area bins

3D fingerprints (usr, usrcat) require molecules with pre-computed conformations. Pass RDKit Mol objects with the conf_id property set rather than bare SMILES strings.

Examples¶

MACCS keys (fixed 166-bit)¶

from autopeptideml.reps.fps import RepEngineSkfp

engine = RepEngineSkfp('maccs')
smiles = [
    'C[C@H](N)C(=O)N[C@@H](CCCNC(=N)N)C(=O)NCC(=O)O',  # Ala-Arg-Gly
    'N[C@@H](Cc1ccccc1)C(=O)N[C@@H](CS)C(=O)O',          # Phe-Cys
]
X = engine.compute_reps(smiles)
print(X.shape)    # (2, 166)
print(engine.dim())  # 166

ECFP via skfp (variable bit size, count variant)¶

engine = RepEngineSkfp('ecfp', fp_size=2048, radius=3, count=True)
X = engine.compute_reps(smiles)
print(X.shape)    # (2, 2048)
print(engine.dim())  # 2048

AtomPair fingerprint¶

engine = RepEngineSkfp('atompair', fp_size=512)
X = engine.compute_reps(smiles)
print(X.shape)    # (2, 512)

Notes¶

RepEngineSkfp and RepEngineFP both live in autopeptideml.reps.fps and share the same RepEngineBase interface.
For ECFP / FCFP via RDKit (the existing path in build_models), continue to use RepEngineFP. RepEngineSkfp('ecfp', ...) is an independent implementation backed by scikit-fingerprints.
The class map is populated lazily: importing autopeptideml.reps.fps does not require scikit-fingerprints to be installed until RepEngineSkfp is actually instantiated.

RepEngineSkfp — scikit-fingerprints Engine¶