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REVIEW article

Front. Pharmacol.
Sec. Experimental Pharmacology and Drug Discovery
Volume 15 - 2024 | doi: 10.3389/fphar.2024.1459954

The role of artificial intelligence in drug screening, drug design, and clinical trials

Provisionally accepted
Yuyuan Wu Yuyuan Wu Lijing Ma Lijing Ma Xinyi Li Xinyi Li Jingpeng Yang Jingpeng Yang Xinyu Rao Xinyu Rao Yiru Hu Yiru Hu Jingyi Xi Jingyi Xi Lin Tao Lin Tao Jianjun Wang Jianjun Wang Shuiping Liu Shuiping Liu *Gongxing Chen Gongxing Chen
  • Hangzhou Normal University, Hangzhou, China

The final, formatted version of the article will be published soon.

    The role of computational tools in drug discovery and development is becoming increasingly important due to the rapid development of computing power and advancements in computational chemistry and biology, improving research efficiency and reducing the costs and potential risks of preclinical and clinical trials. Machine learning, especially deep learning, a subfield of artificial intelligence (AI), has demonstrated significant advantages in drug discovery and development, including high-throughput and virtual screening, ab initio design of drug molecules, and solving difficult organic syntheses. This review summarizes AI technologies used in drug discovery and development, including their roles in drug screening, design, and solving the challenges of clinical trials. Finally, it discusses the challenges of drug discovery and development based on AI technologies, as well as potential future directions.

    Keywords: artificial intelligence, Drug Discovery, drug screening, drug design, clinical trials

    Received: 09 Jul 2024; Accepted: 11 Nov 2024.

    Copyright: © 2024 Wu, Ma, Li, Yang, Rao, Hu, Xi, Tao, Wang, Liu and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Shuiping Liu, Hangzhou Normal University, Hangzhou, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.