Wu Hongjie, Liu Junkai, Zhang Runhua, Lu Yaoyao, Cui Guozeng, Cui Zhiming, Ding Yijie
School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China.
Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China.
Fundam Res. 2024 Mar 11;4(4):715-737. doi: 10.1016/j.fmre.2024.02.011. eCollection 2024 Jul.
Drug discovery is costly and time consuming, and modern drug discovery endeavors are progressively reliant on computational methodologies, aiming to mitigate temporal and financial expenditures associated with the process. In particular, the time required for vaccine and drug discovery is prolonged during emergency situations such as the coronavirus 2019 pandemic. Recently, the performance of deep learning methods in drug virtual screening has been particularly prominent. It has become a concern for researchers how to summarize the existing deep learning in drug virtual screening, select different models for different drug screening problems, exploit the advantages of deep learning models, and further improve the capability of deep learning in drug virtual screening. This review first introduces the basic concepts of drug virtual screening, common datasets, and data representation methods. Then, large numbers of common deep learning methods for drug virtual screening are compared and analyzed. In addition, a dataset of different sizes is constructed independently to evaluate the performance of each deep learning model for the difficult problem of large-scale ligand virtual screening. Finally, the existing challenges and future directions in the field of virtual screening are presented.
药物研发成本高昂且耗时，现代药物研发工作越来越依赖于计算方法，旨在减少与该过程相关的时间和资金支出。特别是在诸如2019年冠状病毒大流行等紧急情况下，疫苗和药物研发所需的时间会延长。最近，深度学习方法在药物虚拟筛选中的表现尤为突出。如何总结药物虚拟筛选中现有的深度学习方法，针对不同的药物筛选问题选择不同的模型，发挥深度学习模型的优势，并进一步提高深度学习在药物虚拟筛选中的能力，已成为研究人员关注的问题。本文综述首先介绍了药物虚拟筛选的基本概念、常用数据集和数据表示方法。然后，对大量用于药物虚拟筛选的常见深度学习方法进行了比较和分析。此外，还独立构建了不同规模的数据集，以评估每个深度学习模型在大规模配体虚拟筛选难题上的性能。最后，介绍了虚拟筛选领域目前存在的挑战和未来方向。
