Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
J Phys Chem Lett. 2021 Mar 18;12(10):2509-2515. doi: 10.1021/acs.jpclett.1c00189. Epub 2021 Mar 15.
The fast and accurate calculation of standard binding free energy has many important applications. Existing methodologies struggle at balancing accuracy and efficiency. We introduce a new method to compute binding free energy using deep generative models and the Bennett acceptance ratio method (DeepBAR). Compared to the rigorous potential of mean force (PMF) approach that requires sampling from intermediate states, DeepBAR is an order-of-magnitude more efficient as demonstrated in a series of host-guest systems. Notably, DeepBAR is exact and does not suffer from approximations for entropic contributions used in methods such as the molecular mechanics energy combined with the generalized Born and surface area continuum solvation (MM/GBSA). We anticipate DeepBAR to be a valuable tool for computing standard binding free energy used in drug design.
标准结合自由能的快速准确计算有许多重要的应用。现有的方法在平衡准确性和效率方面存在困难。我们引入了一种新的方法，使用深度生成模型和 Bennett 接受率方法（DeepBAR）来计算结合自由能。与需要从中介状态采样的严格势能平均力（PMF）方法相比，DeepBAR 的效率要高出一个数量级，这在一系列主体-客体系统中得到了证明。值得注意的是，DeepBAR 是精确的，不会受到诸如分子力学能量与广义 Born 和表面面积连续体溶剂化（MM/GBSA）中使用的熵贡献近似的影响。我们预计 DeepBAR 将成为药物设计中计算标准结合自由能的有用工具。
