Zhu Zhengdan, Deng Zhenfeng, Wang Qinrui, Wang Yuhang, Zhang Duo, Xu Ruihan, Guo Lvjun, Wen Han
Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
Beijing Institute of Big Data Research, Beijing, China.
Front Pharmacol. 2022 Jun 28;13:939555. doi: 10.3389/fphar.2022.939555. eCollection 2022.
Ion channels are expressed in almost all living cells, controlling the in-and-out communications, making them ideal drug targets, especially for central nervous system diseases. However, owing to their dynamic nature and the presence of a membrane environment, ion channels remain difficult targets for the past decades. Recent advancement in cryo-electron microscopy and computational methods has shed light on this issue. An explosion in high-resolution ion channel structures paved way for structure-based rational drug design and the state-of-the-art simulation and machine learning techniques dramatically improved the efficiency and effectiveness of computer-aided drug design. Here we present an overview of how simulation and machine learning-based methods fundamentally changed the ion channel-related drug design at different levels, as well as the emerging trends in the field.
离子通道几乎在所有活细胞中都有表达,控制着细胞内外的通讯,使其成为理想的药物靶点,尤其是对于中枢神经系统疾病。然而,由于其动态特性以及膜环境的存在,在过去几十年里,离子通道仍然是难以攻克的靶点。冷冻电子显微镜和计算方法的最新进展为解决这一问题带来了曙光。高分辨率离子通道结构的大量涌现为基于结构的合理药物设计铺平了道路,而最先进的模拟和机器学习技术极大地提高了计算机辅助药物设计的效率和效果。在此,我们概述了基于模拟和机器学习的方法如何在不同层面上从根本上改变了与离子通道相关的药物设计,以及该领域的新趋势。