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', MorganFingerprintTransformer()), ('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

Installation

Users can install latest tagged release from pip

pip install scikit-mol

or from conda-forge

conda install -c conda-forge scikit-mol

The conda forge package should get updated shortly after a new tagged release on pypi.

Bleeding edge

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

Documentation

Example notebooks and API documentation are now hosted on https://scikit-mol.readthedocs.io

• Basic Usage and fingerprint transformers
• Descriptor transformer
• Pipelining with Scikit-Learn classes
• Molecular standardization
• Sanitizing SMILES input
• Integrated hyperparameter tuning of Scikit-Learn estimator and Scikit-Mol transformer
• Using parallel execution to speed up descriptor and fingerprint calculations
• Using skopt for hyperparameter tuning
• Testing different fingerprints as part of the hyperparameter optimization
• Using pandas output for easy feature importance analysis and combine pre-existing values with new computations
• Working with pipelines and estimators in safe inference mode for handling prediction on batches with invalid smiles or molecules
• Creating custom fingerprint transformers
• Estimating applicability domain using feature based estimators

We also put a software note on ChemRxiv. https://doi.org/10.26434/chemrxiv-2023-fzqwd

Other use-examples

Scikit-Mol has been featured in blog-posts or used in research, some examples which are listed below:

• Useful ML package for cheminformatics iwatobipen.wordpress.com
• Boosted trees Data_in_life_blog
• Konnektor: A Framework for Using Graph Theory to Plan Networks for Free Energy Calculations
• Moldrug algorithm for an automated ligand binding site exploration by 3D aware molecular enumerations
• RandomNets Improve Neural Network Regression Performance via Implicit Ensembling
• WAE-DTI: Ensemble-based architecture for drug–target interaction prediction using descriptors and embeddings
• Data Driven Estimation of Molecular Log-Likelihood using Fingerprint Key Counting
• AUTONOMOUS DRUG DISCOVERY
• DrugGym: A testbed for the economics of autonomous drug discovery

Roadmap and Contributing

Help wanted! Are you a PhD student that want a "side-quest" to procrastinate your thesis writing or are you interested in computational chemistry, cheminformatics or simply with an interest in QSAR modelling, Python Programming open-source software? Do you want to learn more about machine learning with Scikit-Learn? Or do you use scikit-mol for your current work and would like to pay a little back to the project and see it improved as well? With a little bit of help, this project can be improved much faster! Reach to me (Esben), for a discussion about how we can proceed.

Currently, we are working on fixing some deprecation warnings, it's not the most exciting work, but it's important to maintain a little. Later on we need to go over the scikit-learn compatibility and update to some of their newer features on their estimator classes. We're also brewing on some feature enhancements and tests, such as new fingerprints and a more versatile standardizer.

There are more information about how to contribute to the project in CONTRIBUTING

BUGS

Probably still, please check issues at GitHub and report there

Contributors

Scikit-Mol has been developed as a community effort with contributions from people from many different companies, consortia, foundations and academic institutions.

Cheminformania Consulting, Aptuit, BASF, Bayer AG, Boehringer Ingelheim, Chodera Lab (MSKCC), EPAM Systems,ETH Zürich, Evotec, Johannes Gutenberg University, Martin Luther University, Odyssey Therapeutics, Open Molecular Software Foundation, Openfree.energy, Polish Academy of Sciences, Productivista, Simulations-Plus Inc., University of Vienna

• Esben Jannik Bjerrum @ebjerrum, esbenbjerrum+scikit_mol@gmail.com
• Carmen Esposito @cespos
• Son Ha @son-ha-264
• Oh-hyeon Choung @Ohyeon5
• Andreas Poehlmann @ap--
• Ya Chen @anya-chen
• Anton Siomchen @asiomchen
• Rafał Bachorz @rafalbachorz
• Adrien Chaton @adrienchaton
• @VincentAlexanderScholz
• @RiesBen
• @enricogandini
• @mikemhenry
• @c-feldmann
• Mieczyslaw Torchala @mieczyslaw
• Kyle Barbary @kbarbary