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

莱斯大学的Cecilia Clementi获得了化学部化学理论、模型和计算方法项目的支持，为大分子系统开发多尺度模型。克莱门蒂教授和她的团队正在开发机器学习工具，将微观模拟的结果和实验数据结合在一起，形成一个数据驱动的建模框架。在过去的几年里，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，产生了海量且不断增加的数据量。克莱门蒂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，以便制定出规范分子系统行为的一般原则。在分子水平上了解化学过程对于从能量存储到药物设计的大量应用是必不可少的。此外，由于用模型表示海量数据集的需求在不同领域具有相似性，克莱门蒂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。她的项目包括开发本科和研究生课程，以及以招收和指导少数族裔学生为重点的外联活动，特别是通过与莱斯大学塔皮亚中心的合作。克莱门蒂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，通过结合微观模拟产生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/机理)的粗粒度动力学模型。通过集成不同的数据源，该建模方法协调了自下而上和自上而下的方法。这种方法生成的功能构建块可以嵌入到更高阶的模拟中，以便弥合与宏观系统的差距。这个模型框架可以作为一个基石，将大量的化学数据集成到定量、机械和可理解的模型中。这样的模型可以解释不同的分子组成如何作为时间和空间的函数在宏观尺度上执行功能时组织和相互作用。特别是，开发的框架被应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体(MHC)蛋白质的结合。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.