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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.

莱斯大学的Cecilia Clementi获得了化学系化学理论、模型和计算方法项目的一个奖项的支持，以开发大分子系统的多尺度模型。 Clementi教授和她的团队正在开发机器学习工具，将微观模拟和实验数据的结果联合收割机结合到数据驱动的建模框架中。在过去几年中，用于实验观测的高通量和高分辨率技术有了巨大的增长。这些进展与高性能技术相结合，在微观水平上模拟分子系统，从而产生大量且不断增加的数据。Clementi教授正在利用这些丰富的数据，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。在分子水平上理解化学过程对于从能量储存到药物设计的大量应用至关重要。此外，由于需要用模型来表示大量数据集在不同领域具有相似性，因此Clementi教授的工作可能会对从基因组学到金融学的各种完全不同的学科产生影响。这项研究影响了学生和研究人员的跨学科社区。她的项目包括本科生和研究生课程的开发，以及专注于招募和指导少数民族学生的外展活动，特别是通过与莱斯大学塔皮亚中心的合作。Clementi教授正在开发一个数据驱动的框架，以设计多分辨率的有效分子模型，以解决现有计算和实验方法目前无法解决的问题。其主要思想是使用最先进的机器学习方法，通过结合微观模拟生成的模拟数据和实验数据，在中尺度上“学习”控制分子系统（结构，热力学和动力学/机制）的粗粒度动力学模型。通过集成不同的数据源，这种建模方法协调了自下而上和自上而下的方法。这种方法产生的功能构建块，可以嵌入到高阶模拟，以弥合差距的宏观系统。这种建模框架可以作为一个基石，以整合大量的化学数据到定量的，机械的和可理解的模型。这样的模型可以解释不同的分子组分如何组织和相互作用，作为时间和空间的函数，在宏观尺度上执行功能。特别是，开发的框架适用于调查一个特定的生物分子过程：结合肽的主要组织相容性复合物（MHC）proteines.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。