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励，他开发了有效预测大量化学物质的热力学性质的方法，以及使用先进的统计和模拟技术来描述这些物质的替代模型。通过物理建模来预测给定化学系统的热力学性质基本上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用于分子模拟。本项目首次将现有的几种统计技术应用于热力学性质估计问题，并探索了几种新的模拟技术。最重要的是，该项目扩展了现有技术的能力，从一些状态的热力学性质取样和计算到大规模化学性质预测和设计所需的指数大的多维空间。优化化学空间热力学性质的能力的提高将对药物和材料设计产生影响。这种能力将使探索新蛋白质、结构异聚物和其他化学复杂的非均相系统的性质变得更加容易，潜在地揭示了其他方式无法发现的新结构和功能框架。例如，所提出的技术将使优化小分子成为可能，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迅速确定哪种提出的分子模型最能描述自然化学系统。本提案中的方法将在GROMACS中实现，GROMACS是一个广泛使用的开源分子动力学软件工具包，使其可供大量研究人员使用。这些发现和工具将被纳入Alchemistry.org，一个分享经典分子系统中自由能计算的技术、例子和方法的门户网站。