D3SC: CDS&E: Learning molecular models from microscopic simulation and experimental data

D3SC：CDS

基本信息

批准号：
1900374
负责人：
Anatoly Kolomeisky
金额：
$ 51万
依托单位：
William Marsh Rice University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-05-01 至 2023-04-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1900374&HistoricalAwards=false
关键词：
D3SC CDS&amp Learning molecular models

项目摘要

Cecilia Clementi of Rice University is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to develop multiscale models for macromolecular systems. Professor Clementi and her group are developing machine learning tools to combine the results from microscopic simulation and experimental data into a data-driven modeling framework. The last several years have seen an immense increase in high-throughput and high-resolution technologies for experimental observation. These advances are combined with high-performance techniques to simulate molecular systems at a microscopic level resulting in vast and ever-increasing amounts of data. Professor Clementi is taking advantage of this abundance of data and uses machine learning to extract information, in order to formulate general principles regulating the behavior of molecular systems. Understanding chemical processes at the molecular level is essential for a large number of applications, from energy storage to drug design. Additionally, as the need to represent massive data sets in terms of a model bears similarity across different fields, Professor Clementi's work may have an impact on a broad range of completely different disciplines from genomics to finance. This research impacts an interdisciplinary community of students and researchers. Her project includes the development of undergraduate and graduate courses, and outreach activities focused in the recruiting and mentoring of minority students, especially through collaboration with the Tapia Center at Rice University.Professor Clementi is developing a data-driven framework to design effective molecular models at multiple resolutions, to address questions currently out of reach to existing computational and experimental approaches. The main idea is to use state-of-the-art machine learning methods to "learn" the coarse-grained dynamical models governing molecular systems (structure, thermodynamics, and kinetics/mechanism) at the mesoscale, by combining simulation data generated from microscopic simulation, and experimental data. By integrating different sources of data, this modeling approach reconciles bottom-up and top-down methods. This approach generates functional building blocks that can be embedded in higher-order simulations in order to bridge the gap to macroscopic systems. This modeling framework may serve as a keystone to integrate vast amounts of chemical data into quantitative, mechanistic and comprehensible models. Such models may explain how different molecular components organize and interact as a function of time and space in performing functions at the macroscopic scale. In particular, the developed framework is applied to investigate one specific biomolecular process: the binding of peptides to Major Histocompatability Complex (MHC) proteins.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

赖斯大学的Cecilia Clementi得到了化学理论，模型和计算方法计划的奖项，以开发大分子系统的多尺度模型。 Clementi教授和她的小组正在开发机器学习工具，以将微观模拟和实验数据的结果结合到数据驱动的建模框架中。在过去的几年中，高通量和高分辨率技术的实验观察技术大大增加了。这些进步与高性能技术相结合，以在微观水平上模拟分子系统，从而产生大量和不断增加的数据。 Clementi教授正在利用这种丰富的数据，并使用机器学习来提取信息，以制定调节分子系统行为的一般原则。了解分子水平的化学过程对于从储能到药物设计的大量应用至关重要。此外，由于在模型方面表示大规模数据集的需求在不同领域都具有相似性，因此克莱门蒂（Clementi）教授的工作可能会影响从基因组学到金融方面的一系列完全不同的学科。这项研究影响了学生和研究人员的跨学科社区。她的项目包括发展本科和研究生课程的发展，以及集中于招募和指导少数族裔学生的活动，尤其是通过与赖斯大学的Tapia中心合作的招聘和指导。多种决议，以解决目前无法解决现有的计算和实验方法的问题。主要思想是通过结合从显微镜产生的仿真数据，使用最先进的机器学习方法来“学习”分子系统（结构，热力学和动力学/机制）的粗粒粒度动力学模型（结构，热力学和动力学/机制）模拟和实验数据。通过集成不同的数据源，这种建模方法可以调解自下而上的方法和自上而下的方法。这种方法生成功能性构建块，可以嵌入高阶模拟中，以弥合差距到宏观系统。该建模框架可以用作将大量化学数据整合到定量，机械和可理解的模型中的基石。这样的模型可以解释不同的分子成分如何在宏观尺度执行功能的时间和空间的函数中组织和相互作用。特别是，已开发的框架用于研究一个特定的生物分子过程：肽与主要组织相容性复合物（MHC）蛋白的结合。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛的评估来支持的。影响审查标准。