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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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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