Highly multidimensional thermodynamic property prediction for chemical design using atomistic simulations

使用原子模拟进行化学设计的高度多维热力学性质预测

• 批准号: 1152786
• 负责人: Michael Shirts
• 金额: $ 41.4万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-05-15 至 2016-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1152786&HistoricalAwards=false
• 关键词: Highly; multidimensional; thermodynamic; property; prediction

Michael Shirts of the University of Virginia is supported by an award from the Chemical Theory, Models and Computational Methods program to develop methods to efficiently predict the thermodynamic properties of large numbers of chemical species and alternative models to describe these species using advanced statistical and simulation techniques. Predicting thermodynamic properties for a given chemical system via physical modeling is fundamentally a statistical estimation problem, but many potentially useful statistical techniques are not currently used in molecular simulation. This project applies several existing statistical techniques to the problem of thermodynamic property estimation for the first time and explores several novel simulation techniques. Most significantly, the project expands the ability of existing techniques for sampling from and computing thermodynamic properties of a few states to the exponentially large multidimensional spaces required for large-scale chemical property prediction and design.Improved capabilities to optimize thermodynamic properties over chemical space would have impact in drug and materials design. Such capabilities would make exploring the properties of new proteins, structured heteropolymers, and other chemically complex heterogeneous systems much easier, potentially revealing new structural and functional frameworks that could not otherwise be discovered. For example, the proposed techniques would make it possible to optimize small molecules to have the tightest possible binding affinity across all single-point mutations of a given viral protein. It would also allow researchers to rapidly identify which proposed molecular models best describe natural chemical systems. The methods in this proposal will be implemented in GROMACS, a widely-used open source package of molecular dynamics software tools, making them accessible to large numbers of researchers. The findings and tools will be incorporated into Alchemistry.org, a web portal for sharing techniques, examples, and methods for free energy computations in classical molecular systems.

弗吉尼亚大学的 Michael Shirts 获得了化学理论、模型和计算方法项目的奖项支持，旨在开发有效预测大量化学物质热力学性质的方法以及使用先进统计和模拟技术描述这些物质的替代模型。 通过物理建模预测给定化学系统的热力学性质从根本上来说是一个统计估计问题，但许多潜在有用的统计技术目前尚未在分子模拟中使用。 该项目首次将几种现有的统计技术应用于热力学性质估计问题，并探索了几种新颖的模拟技术。 最重要的是，该项目将现有技术从几种状态采样和计算热力学性质的能力扩展到大规模化学性质预测和设计所需的指数级大多维空间。在化学空间上优化热力学性质的能力得到提高对药物和材料设计的影响。这种能力将使探索新蛋白质、结构化杂聚物和其他化学复杂异质系统的特性变得更加容易，有可能揭示否则无法发现的新结构和功能框架。 例如，所提出的技术将可以优化小分子，使其在给定病毒蛋白的所有单点突变中具有尽可能紧密的结合亲和力。 它还将使研究人员能够快速确定哪些提议的分子模型最能描述天然化学系统。 该提案中的方法将在 GROMACS（一种广泛使用的分子动力学软件工具开源包）中实施，使大量研究人员能够使用它们。研究结果和工具将被纳入 Alchemistry.org，这是一个共享经典分子系统中自由能计算的技术、示例和方法的门户网站。