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Bayesian Methods in Virtual Screening and Chemical Biology

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Chemoinformatics and Computational Chemical Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 672))

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.

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Authors and Affiliations

  1. Gorlaeus Laboratories, Center for Drug Research, Medicinal Chemistry, Universiteit Leiden/Amsterdam, Leiden, The Netherlands

    Andreas Bender (Faculty of Science)

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  1. Andreas Bender
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Editors and Affiliations

  1. , Department of Life Science Informatics, Rheinische Friedrich-Wilhelms-Universitä, Dahlmannstr. 2, Bonn, 53113, Germany

    Jürgen Bajorath

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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

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  • DOI: https://doi.org/10.1007/978-1-60761-839-3_7

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  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-60761-838-6

  • Online ISBN: 978-1-60761-839-3

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