Highly multidimensional thermodynamic property prediction for chemical design using atomistic simulations

弗吉尼亚大学的Michael Shirts获得了化学理论，模型和计算方法计划的奖励，以开发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。 本计画首次将数种现有的统计技术应用于热力学性质估计问题，并探讨数种新颖的模拟技术。 最重要的是，该项目扩展了现有技术的能力，从采样和计算热力学性质的几个状态到指数大的多维空间所需的大规模化学性质预测和设计。改进的能力，以优化热力学性质的化学空间将在药物和材料设计的影响。这种能力将使探索新蛋白质，结构杂聚物和其他化学复杂的异质系统的特性变得更加容易，可能揭示新的结构和功能框架，否则无法发现。 例如，所提出的技术将有可能优化小分子，使其在给定病毒蛋白的所有单点突变中具有最紧密的可能结合亲和力。 它还将使研究人员能够快速确定哪些提出的分子模型最好地描述了自然化学系统。 该提案中的方法将在GROMACS中实现，GROMACS是一种广泛使用的分子动力学软件工具的开源包，使大量研究人员可以使用它们。这些发现和工具将被纳入Alchemistry.org，这是一个门户网站，用于分享经典分子系统中自由能计算的技术、示例和方法。

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.