作者
Tai-Sung Lee, Bryce K Allen, Timothy J Giese, Zhenyu Guo, Pengfei Li, Charles Lin, T Dwight McGee Jr, David A Pearlman, Brian K Radak, Yujun Tao, Hsu-Chun Tsai, Huafeng Xu, Woody Sherman, Darrin M York
发表日期
2020/9/16
期刊
Journal of Chemical Information and Modeling
卷号
60
期号
11
页码范围
5595-5623
出版商
American Chemical Society
简介
Predicting protein–ligand binding affinities and the associated thermodynamics of biomolecular recognition is a primary objective of structure-based drug design. Alchemical free energy simulations offer a highly accurate and computationally efficient route to achieving this goal. While the AMBER molecular dynamics package has successfully been used for alchemical free energy simulations in academic research groups for decades, widespread impact in industrial drug discovery settings has been minimal because of the previous limitations within the AMBER alchemical code, coupled with challenges in system setup and postprocessing workflows. Through a close academia-industry collaboration we have addressed many of the previous limitations with an aim to improve accuracy, efficiency, and robustness of alchemical binding free energy simulations in industrial drug discovery applications. Here, we highlight …
引用总数
20202021202220232024643597369
学术搜索中的文章
TS Lee, BK Allen, TJ Giese, Z Guo, P Li, C Lin… - Journal of Chemical Information and Modeling, 2020