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Review
. 2024 Jul;181(14):2371-2384.
doi: 10.1111/bph.16140. Epub 2023 May 26.

The application of artificial intelligence to accelerate G protein-coupled receptor drug discovery

Anh T N Nguyen  1 , Diep T N Nguyen  2 , Huan Yee Koh  1   3 , Jason Toskov  4 , William MacLean  4 , Andrew Xu  4 , Daokun Zhang  1   3 , Geoffrey I Webb  3 , Lauren T May  1 , Michelle L Halls  1
Affiliations
Review

The application of artificial intelligence to accelerate G protein-coupled receptor drug discovery

Anh T N Nguyen et al. Br J Pharmacol. 2024 Jul.

Abstract

The application of artificial intelligence (AI) approaches to drug discovery for G protein-coupled receptors (GPCRs) is a rapidly expanding area. Artificial intelligence can be used at multiple stages during the drug discovery process, from aiding our understanding of the fundamental actions of GPCRs to the discovery of new ligand-GPCR interactions or the prediction of clinical responses. Here, we provide an overview of the concepts behind artificial intelligence, including the subfields of machine learning and deep learning. We summarise the published applications of artificial intelligence to different stages of the GPCR drug discovery process. Finally, we reflect on the benefits and limitations of artificial intelligence and share our vision for the exciting potential for further development of applications to aid GPCR drug discovery. In addition to making the drug discovery process "faster, smarter and cheaper," we anticipate that the application of artificial intelligence will create exciting new opportunities for GPCR drug discovery. LINKED ARTICLES: This article is part of a themed issue Therapeutic Targeting of G Protein-Coupled Receptors: hot topics from the Australasian Society of Clinical and Experimental Pharmacologists and Toxicologists 2021 Virtual Annual Scientific Meeting. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v181.14/issuetoc.

Keywords: G protein‐coupled receptor; artificial intelligence; deep learning; drug discovery; machine learning.

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References

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