Zhao Lingling, Zhu Yan, Wang Junjie, Wen Naifeng, Wang Chunyu, Cheng Liang
Faculty of Computing, Harbin Institute of Technology, Harbin, China.
Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
Comput Struct Biotechnol J. 2022 Jun 3;20:2831-2838. doi: 10.1016/j.csbj.2022.06.004. eCollection 2022.
The task of identifying protein-ligand interactions (PLIs) plays a prominent role in the field of drug discovery. However, it is infeasible to identify potential PLIs via costly and laborious experiments. There is a need to develop PLI computational prediction approaches to speed up the drug discovery process. In this review, we summarize a brief introduction to various computation-based PLIs. We discuss these approaches, in particular, machine learning-based methods, with illustrations of different emphases based on mainstream trends. Moreover, we analyzed three research dynamics that can be further explored in future studies.
识别蛋白质-配体相互作用（PLIs）的任务在药物发现领域中起着重要作用。然而，通过昂贵且费力的实验来识别潜在的PLIs是不可行的。因此，需要开发PLI计算预测方法来加速药物发现过程。在这篇综述中，我们简要总结了各种基于计算的PLIs。我们讨论了这些方法，特别是基于机器学习的方法，并根据主流趋势进行了不同重点的阐述。此外，我们分析了未来研究中可以进一步探索的三个研究动态。
