Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China.
Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China.
Drug Discov Today. 2022 Jul;27(7):1796-1814. doi: 10.1016/j.drudis.2021.10.010. Epub 2021 Oct 27.
Drug repositioning is an attractive strategy for discovering new therapeutic uses for approved or investigational drugs, with potentially shorter development timelines and lower development costs. Various computational methods have been used in drug repositioning, promoting the efficiency and success rates of this approach. Recently, deep learning (DL) has attracted wide attention for its potential in target prediction and drug repositioning. Here, we provide an overview of the basic principles of commonly used DL architectures and their applications in target prediction and drug repositioning, and discuss possible ways of dealing with current challenges to help achieve its expected potential for drug repositioning.
药物重定位是发现已批准或在研药物新治疗用途的一种有吸引力的策略，具有潜在的更短的开发时间和更低的开发成本。各种计算方法已被用于药物重定位，提高了这种方法的效率和成功率。最近，深度学习（DL）因其在靶标预测和药物重定位方面的潜力而受到广泛关注。在这里，我们提供了常用 DL 架构的基本原理及其在靶标预测和药物重定位中的应用的概述，并讨论了应对当前挑战的可能方法，以帮助实现其在药物重定位方面的预期潜力。
