Welcome to scikit-fingerprints documentation!#

A scikit-learn compatible library for efficient computation of molecular fingerprints.

Main features:

  • scikit-learn compatible

  • feature-rich, with >30 fingerprints

  • parallelization

  • sparse matrix support

  • commercial-friendly MIT license

Easiest way to get started is to install it with pip:

pip install scikit-fingerprints

Then, see the quickstart below, or go for more detailed Examples.

Contents

Quickstart#

Most fingerprints are based on molecular graphs (topological, 2D-based), and you can use SMILES input directly:

from skfp.fingerprints import AtomPairFingerprint

smiles_list = ["O=S(=O)(O)CCS(=O)(=O)O", "O=C(O)c1ccccc1O"]

atom_pair_fingerprint = AtomPairFingerprint()

X = atom_pair_fingerprint.transform(smiles_list)
print(X)

For fingerprints using conformers (conformational, 3D-based), you need to create molecules first and compute conformers. Those fingerprints have requires_conformers attribute set to True.

from skfp.preprocessing import ConformerGenerator, MolFromSmilesTransformer
from skfp.fingerprints import WHIMFingerprint

smiles_list = ["O=S(=O)(O)CCS(=O)(=O)O", "O=C(O)c1ccccc1O"]

mol_from_smiles = MolFromSmilesTransformer()
conf_gen = ConformerGenerator()
fp = WHIMFingerprint()
print(fp.requires_conformers)  # True

mols_list = mol_from_smiles.transform(smiles_list)
mols_list = conf_gen.transform(mols_list)

X = fp.transform(mols_list)
print(X)

You can also use scikit-learn functionalities like pipelines, feature unions etc. to build complex workflows. Popular datasets, e.g. from MoleculeNet benchmark, can be loaded directly.

from skfp.datasets.moleculenet import load_clintox
from skfp.metrics import multioutput_auroc_score, extract_pos_proba
from skfp.model_selection import scaffold_train_test_split
from skfp.fingerprints import ECFPFingerprint, MACCSFingerprint
from skfp.preprocessing import MolFromSmilesTransformer

from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import make_pipeline, make_union


smiles, y = load_clintox()
smiles_train, smiles_test, y_train, y_test = scaffold_train_test_split(
    smiles, y, test_size=0.2
)

pipeline = make_pipeline(
    MolFromSmilesTransformer(),
    make_union(ECFPFingerprint(count=True), MACCSFingerprint()),
    RandomForestClassifier(random_state=0),
)
pipeline.fit(smiles_train, y_train)

y_pred_proba = pipeline.predict_proba(smiles_test)
y_pred_proba = extract_pos_proba(y_pred_proba)
auroc = multioutput_auroc_score(y_test, y_pred_proba)
print(f"AUROC: {auroc:.2%}")
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