title: Bayesian methods in virtual screening and chemical biology.
abstract: 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.
authors.affiliation: Gorlaeus Laboratories, Center for Drug Research, Medicinal Chemistry, Universiteit Leiden/Amsterdam, Leiden, The Netherlands.
authors.forename: Andreas
authors.initials: A
authors.lastname: Bender
mesh_terms: D001499:Bayes Theorem; D001695:Biology; D019295:Computational Biology / Q000379:methods*; D015195:Drug Design; D048088:Informatics; D008024:Ligands
chemical_list: D008024:Ligands