Suppr超能文献

基于数据的药物发现中的毒性预测:现状与未来方向。

Data-driven toxicity prediction in drug discovery: Current status and future directions.

机构信息

Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008 Hunan, PR China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008 Hunan, PR China; The Hunan Institute of Pharmacy Practice and Clinical Research, Changsha 410008 Hunan, PR China.

Hunan Institute for Drug Control, Changsha 410001 Hunan, PR China.

出版信息

Drug Discov Today. 2024 Nov;29(11):104195. doi: 10.1016/j.drudis.2024.104195. Epub 2024 Sep 30.

Abstract

Early toxicity assessment plays a vital role in the drug discovery process on account of its significant influence on the attrition rate of candidates. Recently, constant upgrading of information technology has greatly promoted the continuous development of toxicity prediction. To give an overview of the current state of data-driven toxicity prediction, we reviewed relevant studies and summarized them in three main respects: the features and difficulties of toxicity prediction, the evolution of modeling approaches, and the available tools for toxicity prediction. For each part, we expound the research status, existing challenges, and feasible solutions. Finally, several new directions and suggestions for toxicity prediction are also put forward.

摘要

早期毒性评估在药物发现过程中起着至关重要的作用,因为它对候选药物的淘汰率有重大影响。最近,信息技术的不断升级极大地促进了毒性预测的不断发展。为了全面了解基于数据的毒性预测的现状,我们回顾了相关研究,并将其总结为三个主要方面:毒性预测的特点和难点、建模方法的演变,以及毒性预测的可用工具。对于每一部分,我们都阐述了研究现状、存在的挑战和可行的解决方案。最后,还提出了毒性预测的几个新方向和建议。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验