ROBERT (Refiner and Optimizer of a Bunch of Existing Regression Tools)

ROBERT is an ensemble of automated machine learning protocols that can be run sequentially through a single command line. The program works for regression and classification problems. Comprehensive workflows have been designed to meet state-of-the-art standards for cheminformatics studies.

Documentation

Full documentation with installation instructions, technical details and examples can be found in Read the Docs.

Don't miss out the latest hands-on tutorials from our YouTube channel!

Developers and help desk

List of main developers and contact emails:

• Juan V. Alegre-Requena. Contact: jv.alegre@csic.es
• David Dalmau Ginesta. Contact: ddalmau@unizar.es

For suggestions and improvements of the code (greatly appreciated!), please reach out through the issues and pull requests options of Github.

License

ROBERT is freely available under an MIT License

Special acknowledgements

J.V.A.R. - The acronym ROBERT is dedicated to ROBERT Paton, who was a mentor to me throughout my years at Colorado State University and who introduced me to the field of cheminformatics. Cheers mate!

D.D.G. - The style of the ROBERT_report.pdf file was created with the help of Oliver Lee (2023, Zysman-Colman group at University of St Andrews).

We really THANK all the testers for their feedback and for participating in the reproducibility tests, including:

• David Valiente (2022-2023, Universidad Miguel Hernández)
• Heidi Klem (2023, Paton group at Colorado State University)
• Iñigo Iribarren (2023, Trujillo group at Trinity College Dublin)
• Guilian Luchini (2023, Paton group at Colorado State University)
• Alex Platt (2023, Paton group at Colorado State University)
• Oliver Lee (2023, Zysman-Colman group at University of St Andrews)
• Xinchun Ran (2023, Yang group at Vanderbilt University)

How to cite ROBERT

If you use any of the ROBERT modules, please include this citation:

• Dalmau, D.; Alegre Requena, J. V. ChemRxiv, 2023, DOI: 10.26434/chemrxiv-2023-k994h.

If you use the AQME module, please include this citation:

• Alegre-Requena et al., AQME: Automated Quantum Mechanical Environments for Researchers and Educators. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2023, 13, e1663.

Additionally, please include the corresponding reference for Scikit-learn and SHAP:

• Pedregosa et al., Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res. 2011, 12, 2825-2830.
• Lundberg et al., From local explanations to global understanding with explainable AI for trees, Nat. Mach. Intell. 2020, 2, 56–67.