Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea.
Faculty of Information Technology, An Giang University, Long Xuyen 880000, Vietnam.
J Chem Inf Model. 2023 Oct 23;63(20):6198-6211. doi: 10.1021/acs.jcim.3c00960. Epub 2023 Oct 11.
Absorption is an important area of research in pharmacochemistry and drug development, because the drug has to be absorbed before any drug effects can occur. Furthermore, the ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profile of drugs can be directly and considerably altered by modulating factors affecting absorption. Many drugs in development fail because of poor absorption. The research and continuous efforts of researchers in recent years have brought many successes and promises in drug absorption property prediction, especially , which helps to reduce the time and cost significantly for screening undesirable drug candidates. In this report, we explicitly provide an overview of recent studies on predicting absorption properties, especially from 2019 to the present, using artificial intelligence. Additionally, we have collected and investigated public databases that support absorption prediction research. On those grounds, we also proposed the challenges and development directions of absorption prediction in the future. We hope this review can provide researchers with valuable guidelines on absorption prediction to facilitate the development of newer approaches in drug discovery.
吸收是药物化学和药物开发的一个重要研究领域,因为药物在产生任何药物作用之前必须被吸收。此外,药物的 ADMET(吸收、分布、代谢、排泄和毒性)特征可以通过调节影响吸收的因素直接和显著改变。许多处于开发阶段的药物因吸收不良而失败。近年来,研究人员的研究和不断努力在药物吸收性质预测方面带来了许多成功和希望,尤其是基于机器学习的方法,这有助于显著减少不良候选药物的筛选时间和成本。在本报告中,我们明确提供了使用人工智能预测吸收性质的最新研究综述,特别是 2019 年至今的研究。此外,我们还收集和调查了支持吸收预测研究的公共数据库。基于这些,我们还提出了未来吸收预测的挑战和发展方向。我们希望本综述能为研究人员提供关于吸收预测的有价值的指导,以促进药物发现中更新方法的发展。