scikit-mol 0.0.2

scikit-learn classes for molecule transformation

scikit-mol

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

Implemented

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

• Utilities
  • CheckSmilesSanitazion

Installation

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 [e-mail]:EBjerrum/scikit-mol.git
pip install -e .

Documentation

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

BUGS

Probably still

TODO

• Unit test coverage of classes
• If possible return same type as input (e.g. List to list, numpy to numpy, pandas Series to pandas series)
• Docstrings for classes and methods
• Make further example notebooks
  • Standalone usage (not in pipeline)
  • Advanced pipelining
  • Hyperparameter optimization via external optimizer e.g. https://scikit-optimize.github.io/stable/

Ideas

• LINGOS transformer

Contributers:

• Esben Jannik Bjerrum, [e-mail]
• Carmen Esposito https://github.com/cespos
• Son Ha, [e-mail]
• Oh-hyeon Choung, [e-mail]
• Andreas Poehlmann, https://github.com/ap--
• Ya Chen, https://github.com/anya-chen