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Machine Learning for In Silico ADMET Prediction

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Book cover Artificial Intelligence in Drug Design

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2390))

Abstract

ADMET (absorption, distribution, metabolism, excretion, and toxicity) describes a drug molecule’s pharmacokinetics and pharmacodynamics properties. ADMET profile of a bioactive compound can impact its efficacy and safety. Moreover, efficacy and safety are considered some of the major causes of clinical attrition in the development of new chemical entities. In past decades, various machine learning or quantitative structure–activity relationship (QSAR) methods have been successfully integrated in the modeling of ADMET. Recent advances have been made in the collection of data and the development of various in silico methods to assess and predict ADMET of bioactive compounds in the early stages of drug discovery and development process.

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Author information

Authors and Affiliations

  1. Bristol Myers Squibb, San Diego, CA, USA

    Lei Jia

  2. Kronos Bio, Cambridge, MA, USA

    Hua Gao

Authors
  1. Lei Jia
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  2. Hua Gao
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Editor information

Editors and Affiliations

  1. Computational Drug Discovery, Evotec (UK) Ltd., Abingdon, Oxfordshire, UK

    Alexander Heifetz

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© 2022 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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Jia, L., Gao, H. (2022). Machine Learning for In Silico ADMET Prediction. In: Heifetz, A. (eds) Artificial Intelligence in Drug Design. Methods in Molecular Biology, vol 2390. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1787-8_20

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  • DOI: https://doi.org/10.1007/978-1-0716-1787-8_20

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1786-1

  • Online ISBN: 978-1-0716-1787-8

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