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Bayesian Optimization in Drug Discovery

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High Performance Computing for Drug Discovery and Biomedicine

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2716))

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Abstract

Drug discovery deals with the search for initial hits and their optimization toward a targeted clinical profile. Throughout the discovery pipeline, the candidate profile will evolve, but the optimization will mainly stay a trial-and-error approach. Tons of in silico methods have been developed to improve and fasten this pipeline. Bayesian optimization (BO) is a well-known method for the determination of the global optimum of a function. In the last decade, BO has gained popularity in the early drug design phase. This chapter starts with the concept of black box optimization applied to drug design and presents some approaches to tackle it. Then it focuses on BO and explains its principle and all the algorithmic building blocks needed to implement it. This explanation aims to be accessible to people involved in drug discovery projects. A strong emphasis is made on the solutions to deal with the specific constraints of drug discovery. Finally, a large set of practical applications of BO is highlighted.

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  1. Evotec SAS (France), Toulouse, France

    Lionel Colliandre & Christophe Muller

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  1. Lionel Colliandre
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    Alexander Heifetz

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Colliandre, L., Muller, C. (2024). Bayesian Optimization in Drug Discovery. In: Heifetz, A. (eds) High Performance Computing for Drug Discovery and Biomedicine. Methods in Molecular Biology, vol 2716. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3449-3_5

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