Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China.
Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410004, Hunan, P. R. China.
Phys Chem Chem Phys. 2020 Feb 14;22(6):3149-3159. doi: 10.1039/c9cp06303j. Epub 2020 Jan 29.
The identification and optimization of lead compounds are inalienable components in drug design and discovery pipelines. As a powerful computational approach for the identification of hits with novel structural scaffolds, structure-based virtual screening (SBVS) has exhibited a remarkably increasing influence in the early stages of drug discovery. During the past decade, a variety of techniques and algorithms have been proposed and tested with different purposes in the scope of SBVS. Although SBVS has been a common and proven technology, it still shows some challenges and problems that are needed to be addressed, where the negative influence regardless of protein flexibility and the inaccurate prediction of binding affinity are the two major challenges. Here, focusing on these difficulties, we summarize a series of combined strategies or workflows developed by our group and others. Furthermore, several representative successful applications from recent publications are also discussed to demonstrate the effectiveness of the combined SBVS strategies in drug discovery campaigns.
先导化合物的鉴定和优化是药物设计和发现管道中不可分割的组成部分。作为一种用于识别具有新颖结构骨架的命中化合物的强大计算方法，基于结构的虚拟筛选（SBVS）在药物发现的早期阶段表现出了显著的影响力。在过去的十年中，已经提出并测试了各种技术和算法，它们具有不同的目的和范围的 SBVS。尽管 SBVS 是一种常见且经过验证的技术，但它仍然存在一些需要解决的挑战和问题，其中蛋白质灵活性的负面影响和结合亲和力的不准确预测是两个主要挑战。在这里，我们重点关注这些困难，总结了我们小组和其他小组开发的一系列组合策略或工作流程。此外，还讨论了最近出版物中的几个有代表性的成功应用案例，以证明组合 SBVS 策略在药物发现中的有效性。
