Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation,Chemical Reviews

当前位置： X-MOL 学术 › Chem. Rev. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation
Chemical Reviews ( IF 51.4 ) Pub Date : 2020-10-02 , DOI: 10.1021/acs.chemrev.0c00534
Sergio Decherchi ₁ , Andrea Cavalli _{1,

2}

Affiliation

Computational studies play an increasingly important role in chemistry and biophysics, mainly thanks to improvements in hardware and algorithms. In drug discovery and development, computational studies can reduce the costs and risks of bringing a new medicine to market. Computational simulations are mainly used to optimize promising new compounds by estimating their binding affinity to proteins. This is challenging due to the complexity of the simulated system. To assess the present and future value of simulation for drug discovery, we review key applications of advanced methods for sampling complex free-energy landscapes at near nonergodicity conditions and for estimating the rate coefficients of very slow processes of pharmacological interest. We outline the statistical mechanics and computational background behind this research, including methods such as steered molecular dynamics and metadynamics. We review recent applications to pharmacology and drug discovery and discuss possible guidelines for the practitioner. Recent trends in machine learning are also briefly discussed. Thanks to the rapid development of methods for characterizing and quantifying rare events, simulation’s role in drug discovery is likely to expand, making it a valuable complement to experimental and clinical approaches.

中文翻译：

通过分子模拟研究药物-靶标结合的热力学和动力学

计算研究在化学和生物物理学中发挥着越来越重要的作用，这主要归功于硬件和算法的改进。在药物发现和开发中，计算研究可以降低将新药推向市场的成本和风险。计算模拟主要用于通过估计其与蛋白质的结合亲和力来优化有前途的新化合物。由于模拟系统的复杂性，这具有挑战性。为了评估模拟在药物发现中的当前和未来价值，我们回顾了先进方法的关键应用，这些方法用于在接近非遍历性条件下对复杂的自由能景观进行采样，以及估计具有药理学意义的非常缓慢的过程的速率系数。我们概述了这项研究背后的统计力学和计算背景，包括引导分子动力学和元动力学等方法。我们回顾了最近在药理学和药物发现方面的应用，并讨论了对从业者可能的指导方针。还简要讨论了机器学习的最新趋势。由于表征和量化罕见事件的方法的快速发展，模拟在药物发现中的作用可能会扩大，使其成为实验和临床方法的宝贵补充。

更新日期：2020-12-09

点击分享查看原文

点击收藏

阅读更多本刊最新论文本刊介绍/投稿指南11