ROBERT is an ensemble of automated machine learning protocols that can be run sequentially through a single command line or a graphical user interface. The program works for regression and classification problems. Comprehensive workflows have been designed to meet state-of-the-art standards for cheminformatics studies, including:
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!
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.
ROBERT is freely available under an MIT License
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)
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.