The Naïve Bayesian Classifier, as well as related classification and regression approaches based on Bayes’ theorem, has experienced increased attention in the cheminformatics world in recent years. In this contribution, we first review the mathematical framework on which Bayes’ methods are built, and then continue to discuss implications of this framework as well as practical experience under which conditions Bayes’ methods give the best performance in virtual screening settings. Finally, we present an overview of applications of Bayes’ methods to both virtual screening and the chemical biology arena, where applications range from bridging phenotypic and mechanistic space of drug action to the prediction of ligand–target interactions.
- Bayes Classifier
- Virtual screening
- Structure-activity relationships
- Mode of action analysis
- Target prediction
- Adverse drug reactions
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Bender, A. (2010). Bayesian Methods in Virtual Screening and Chemical Biology. In: Bajorath, J. (eds) Chemoinformatics and Computational Chemical Biology. Methods in Molecular Biology, vol 672. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-60761-839-3_7
Publisher Name: Humana Press, Totowa, NJ
Print ISBN: 978-1-60761-838-6
Online ISBN: 978-1-60761-839-3
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