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Artificial Intelligence in ADME Property Prediction

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Computational Drug Discovery and Design

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2714))

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Abstract

Absorption, distribution, metabolism, excretion (ADME) are key properties of a small molecule that govern pharmacokinetic profiles and impact its efficacy and safety. Computational methods such as machine learning and artificial intelligence have gained significant interest in both academic and industrial settings to predict pharmacokinetic properties of small molecules. These methods are applied in drug discovery to optimize chemical libraries, prioritize hits from biological screens, and optimize ADME properties of lead molecules. In the recent years, the drug discovery community witnessed the use of a range of neural network architectures such as deep neural networks, recurrent neural networks, graph neural networks, and transformer neural networks, which marked a paradigm shift in computer-aided drug design and development. This chapter discusses recent developments with an emphasis on their application to predict ADME properties.

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Acknowledgments

We thank Edward Kerns, Kimloan Nguyen, Kyeong Ri Yu, Jordan Williams, Md Kabir, Rintaro Kato, Eric Gonzalez, Nao Katori-Torimoto, Paul Shinn, Misha Itkin, Ewy Mathé, Dac-Trung Nguyen, Jorge Neyra, Noel Southall, Sankalp Jain, Alexey Zakharov, Tuan Xu, Ruili Huang, and Hongmao Sun for their valuable contributions to in silico and in vitro ADME research and the development of ADME@NCATS prediction platform.

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Authors and Affiliations

  1. National Center for Advancing Translational Sciences, Rockville, MD, USA

    Vishal B. Siramshetty, Xin Xu & Pranav Shah

  2. Department of Safety Assessment, Genentech, Inc., South San Francisco, CA, USA

    Vishal B. Siramshetty

Authors
  1. Vishal B. Siramshetty
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  2. Xin Xu
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  3. Pranav Shah
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Corresponding author

Correspondence to Pranav Shah .

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Editors and Affiliations

  1. Department of Basic and Applied Sciences, Dayananda Sagar University, Bangalore, Karnataka, India

    Mohini Gore

  2. Department of Botany, Rajaram College Kolhapur, Kolhapur, Maharashtra, India

    Umesh B. Jagtap

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© 2024 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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Siramshetty, V.B., Xu, X., Shah, P. (2024). Artificial Intelligence in ADME Property Prediction. In: Gore, M., Jagtap, U.B. (eds) Computational Drug Discovery and Design. Methods in Molecular Biology, vol 2714. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3441-7_17

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  • DOI: https://doi.org/10.1007/978-1-0716-3441-7_17

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3440-0

  • Online ISBN: 978-1-0716-3441-7

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