The prediction of protein–ligand unbinding for modern drug discovery

Qianqian Zhang, Nannan Zhao, Xiaoxiao Meng, Fansen Yu, Xiaojun Yao, Huanxiang Liu

研究成果: Review article同行評審

13 引文 斯高帕斯(Scopus)

摘要

Introduction: Drug–target thermodynamic and kinetic information have perennially important roles in drug design. The prediction of protein–ligand unbinding, which can provide important kinetic information, in experiments continues to face great challenges. Uncovering protein–ligand unbinding through molecular dynamics simulations has become efficient and inexpensive with the progress and enhancement of computing power and sampling methods. Areas covered: In this review, various sampling methods for protein–ligand unbinding and their basic principles are firstly briefly introduced. Then, their applications in predicting aspects of protein–ligand unbinding, including unbinding pathways, dissociation rate constants, residence time and binding affinity, are discussed. Expert opinion: Although various sampling methods have been successfully applied in numerous systems, they still have shortcomings and deficiencies. Most enhanced sampling methods require researchers to possess a wealth of prior knowledge of collective variables or reaction coordinates. In addition, most systems studied at present are relatively simple, and the study of complex systems in real drug research remains greatly challenging. Through the combination of machine learning and enhanced sampling methods, prediction accuracy can be further improved, and some problems encountered in complex systems also may be solved.

原文English
頁(從 - 到)191-205
頁數15
期刊Expert Opinion on Drug Discovery
17
發行號2
DOIs
出版狀態Published - 2022
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