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通过电荷重整化优化多点 λ-动力学的吞吐量。

Optimizing Multisite λ-Dynamics Throughput with Charge Renormalization.

机构信息

Department of Biochemistry and Molecular Biology, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States.

Department of Medicinal Chemistry College of Pharmacy, University of Michigan, Ann Arbor, Michigan 48109, United States.

出版信息

J Chem Inf Model. 2022 Mar 28;62(6):1479-1488. doi: 10.1021/acs.jcim.2c00047. Epub 2022 Mar 14.

Abstract

With the ability to sample combinations of alchemical perturbations at multiple sites off a small molecule core, multisite λ-dynamics (MSλD) has become an attractive alternative to conventional alchemical free energy methods for exploring large combinatorial chemical spaces. However, current software implementations dictate that combinatorial sampling with MSλD must be performed with a multiple topology model (MTM), which is nontrivial to create by hand, especially for a series of ligand analogues which may have diverse functional groups attached. This work introduces an automated workflow, referred to as , to assist in the creation of a MTM for use with MSλD. One approach for partitioning partial atomic charges between ligands to create a MTM, called charge renormalization, is also presented and rigorously evaluated. We find that greatly accelerates the preparation of MSλD ready-to-use files and that charge renormalization can provide a successful approach for MTM generation, as long as bookending calculations are applied to correct small differences introduced by charge renormalization. Charge renormalization also facilitates the use of many different force field parameters with MSλD, broadening the applicability of MSλD for computer-aided drug design.

摘要

通过在小分子核心的多个位点上采样组合的化学扰动,多位点 λ 动力学(MSλD)已经成为探索大规模组合化学空间的传统自由能方法的有吸引力的替代方法。然而,当前的软件实现规定,MSλD 的组合采样必须使用多拓扑模型(MTM)进行,这很难手动创建,特别是对于一系列可能具有不同功能基团的配体类似物。这项工作引入了一种自动化工作流程,称为 ,以协助创建用于 MSλD 的 MTM。还提出并严格评估了一种用于在配体之间划分部分原子电荷以创建 MTM 的方法,称为电荷再归一化。我们发现 大大加快了 MSλD 准备使用文件的准备速度,并且只要应用端到端计算来纠正电荷再归一化引入的小差异,电荷再归一化就可以为 MTM 生成提供成功的方法。电荷再归一化还促进了 MSλD 与许多不同力场参数的使用,从而拓宽了 MSλD 在计算机辅助药物设计中的适用性。

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J Chem Inf Model. 2022 Mar 28;62(6):1479-1488. doi: 10.1021/acs.jcim.2c00047. Epub 2022 Mar 14.

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