A comprehensive review of artificial intelligence for pharmacology research

Bing Li; Kan Tan; Angelyn R Lao; Haiying Wang; Huiru Zheng; Le Zhang

doi:10.3389/fgene.2024.1450529

A comprehensive review of artificial intelligence for pharmacology research

Front Genet. 2024 Sep 3:15:1450529. doi: 10.3389/fgene.2024.1450529. eCollection 2024.

Authors

Bing Li¹, Kan Tan¹, Angelyn R Lao², Haiying Wang³, Huiru Zheng³, Le Zhang¹

Affiliations

¹ College of Computer Science, Sichuan University, Chengdu, China.
² Department of Mathematics and Statistics, De La Salle University, Manila, Philippines.
³ School of Computing, Ulster University, Belfast, United Kingdom.

Abstract

With the innovation and advancement of artificial intelligence, more and more artificial intelligence techniques are employed in drug research, biomedical frontier research, and clinical medicine practice, especially, in the field of pharmacology research. Thus, this review focuses on the applications of artificial intelligence in drug discovery, compound pharmacokinetic prediction, and clinical pharmacology. We briefly introduced the basic knowledge and development of artificial intelligence, presented a comprehensive review, and then summarized the latest studies and discussed the strengths and limitations of artificial intelligence models. Additionally, we highlighted several important studies and pointed out possible research directions.

Keywords: artificial intelligence; clinical pharmacology; compound pharmacokinetic prediction; drug discovery; pharmacology.

Publication types

Review

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by grants from National Science and Technology Major Project under Grant (2021YFF1201200), in part by the National Natural Science Foundation of China under Grant (62372316) and in part by Sichuan Science and Technology Program key project under Grant (2024YFHZ0091).