Scikit-Learn classes for molecular vectorization using RDKit

The intended usage is to be able to add molecular vectorization directly into scikit-learn pipelines, so that the final model directly predict on RDKit molecules or SMILES strings

As example with the needed scikit-learn and -mol imports and RDKit mol objects in the mol_list_train and _test lists:

pipe = Pipeline([('mol_transformer', MorganTransformer()), ('Regressor', Ridge())])
pipe.fit(mol_list_train, y_train)
pipe.score(mol_list_test, y_test)
pipe.predict([Chem.MolFromSmiles('c1ccccc1C(=O)C')])

>>> array([4.93858815])

The scikit-learn compatibility should also make it easier to include the fingerprinting step in hyperparameter tuning with scikit-learns utilities

The first draft for the project was created at the RDKIT UGM 2022 hackathon 2022-October-14

• Transformer Classes
  • SmilesToMol
  • Desc2DTransformer
  • MACCSTransformer
  • RDKitFPTransformer
  • AtomPairFingerprintTransformer
  • TopologicalTorsionFingerprintTransformer
  • MorganTransformer

• Utilities
  • CheckSmilesSanitazion

Users can install latest tagged release from pip

pip install scikit-mol

Bleeding edge

pip install git+https://github.com:EBjerrum/scikit-mol.git

Developers

git clone [email protected]:EBjerrum/scikit-mol.git
pip install -e .

None yet, but there are some # %% delimted examples in the notebooks directory that have some demonstrations

Probably still

• Add rest of RDKit fingerprints
• Integration tests
• Docstrings for classes and methods
  • Numpy style
• Make further example notebooks
  • Standalone usage (not in pipeline)
  • Advanced pipelining
  • Hyperparameter optimization via external optimizer e.g. https://scikit-optimize.github.io/stable/

• Esben Jannik Bjerrum, [email protected]
• Carmen Esposito @cespos
• Son Ha, [email protected]
• Oh-hyeon Choung, [email protected]
• Andreas Poehlmann, @ap--
• Ya Chen, @anya-chen
• Rafał Bachorz @rafalbachorz