III: Medium: De Rham-Hodge theory modeling and learning of biomolecular data

III: 媒介：De Rham-Hodge 理论建模和生物分子数据学习

• 批准号: 1900473
• 负责人: Guowei Wei
• 金额: $ 118.44万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1900473&HistoricalAwards=false
• 关键词: III; Medium; De; Rham; Hodge

Understanding the rules of life is the major mission of biological sciences in the 21st Century. The availability of massive biological data and the recent advances in computational algorithms have paved the way for biological sciences to transition from a qualitative, phenomenological and descriptive to a quantitative, analytical and predictive approach. However, this transition is hindered by tremendous structural complexity and excessively large datasets. For example, even all the world's computers put together do not have enough power to design drugs automatically because of the structural complexity of protein-drug interactions, excessively large datasets associated with drug configurations, and the high dimensionality of involved molecular simulation and/or machine learning. These challenges will be addressed by innovative mathematical strategies in the present project. A team from mathematics and computer science at Michigan State University will turn sophisticated mathematics into computational algorithms that create simplified representations of complex biomolecules or their interactions. As a result, deep learning and other types of machine learning can be efficiently carried out to extract the structure-function relationship from massive and diverse biomolecular datasets. This information will be extremely valuable for revealing the rules of life and for design new biomolecules, including biomedicine, which ultimately tests our understanding of the biomolecular world and brings a direct benefit to human health. Additionally, this project will support the development of undergraduate and graduate-level courses on computational biophysics and machine learning at Michigan State University. Finally, this research will facilitate the cross-disciplinary training of the next generation researchers who are experts on advanced mathematics, computer algorithms, and molecular-level biology.The objective of the present project is to develop novel de Rham-Hodge theory-based approaches to revolutionize the current practice in biomolecular data analysis and modeling. The de Rham-Hodge theory is a hallmark of the 20th Century?s mathematics that has had a great impact in modern mathematics, quantum physics, and computer science. The investigators will introduce for the first time the de Rham-Hodge theory to reduce the structural complexity of biomolecules. Additionally, the research team will propose the persistent de Rham-Hodge theory and element-specific de Rham-Hodge theory for the first time to properly encode chemical and biological information in biomolecular data representation. These methods will be carefully integrated with advanced machine learning or deep learning algorithms to reveal biomolecular structure-function relationships. Moreover, the investigators will extensively validate the proposed methods on a variety of datasets, such as protein binding to the proteins, ligands, DNA and RNA, protein folding stability changes upon mutation, drug toxicity, solvation, solubility, and partition coefficient. Finally, user-friendly software packages and online servers will be developed using parallel and GPU architectures for researchers who are not formally trained in advanced mathematics or sophisticated machine learning.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.

认识生命规律是21世纪生物科学的主要使命。大量生物数据的可用性和计算算法的最新进展为生物科学从定性、现象学和描述性方法过渡到定量、分析和预测方法铺平了道路。然而，这种转变受到巨大的结构复杂性和过大的数据集的阻碍。例如，由于蛋白质-药物相互作用的结构复杂性、与药物配置相关的过大数据集以及所涉及的分子模拟和/或机器学习的高维性，即使将世界上所有的计算机放在一起也没有足够的能力来自动设计药物。这些挑战将在本项目中通过创新的数学策略来解决。密歇根州立大学的一个数学和计算机科学团队将把复杂的数学转化为计算算法，从而创建复杂生物分子或其相互作用的简化表示。因此，可以有效地进行深度学习和其他类型的机器学习，以从大量和多样化的生物分子数据集中提取结构-功能关系。这些信息对于揭示生命的规律和设计新的生物分子，包括生物医学，最终考验我们对生物分子世界的理解，并为人类健康带来直接利益，将是非常有价值的。此外，该项目将支持密歇根州立大学本科生和研究生水平的计算生物物理学和机器学习课程的开发。最后，这项研究将促进下一代研究人员的跨学科培训，他们是高等数学，计算机算法和分子水平biology.The本项目的目标是开发新的de Rham-Hodge理论为基础的方法，以彻底改变目前的做法，在生物分子数据分析和建模的专家。德拉姆-霍奇理论是20世纪世纪的标志？它对现代数学、量子物理学和计算机科学产生了巨大的影响。研究人员将首次引入de Rham-Hodge理论，以减少生物分子的结构复杂性。此外，研究小组将首次提出持久的de Rham-Hodge理论和元素特异性de Rham-Hodge理论，以正确编码生物分子数据表示中的化学和生物信息。这些方法将与先进的机器学习或深度学习算法仔细整合，以揭示生物分子的结构-功能关系。此外，研究人员将在各种数据集上广泛验证所提出的方法，例如蛋白质与蛋白质，配体，DNA和RNA的结合，突变后蛋白质折叠稳定性的变化，药物毒性，溶剂化，溶解度和分配系数。最后，将使用并行和GPU架构开发用户友好的软件包和在线服务器，供未接受过高等数学或复杂机器学习正式培训的研究人员使用。该奖项反映了NSF的法定使命，并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，被认为值得支持。

项目成果

EVOLUTIONARY DE RHAM-HODGE METHOD.

• DOI: 10.3934/dcdsb.2020257
• 发表时间: 2021-07
• 期刊: Discrete and continuous dynamical systems. Series B
• 作者: Chen J;Zhao R;Tong Y;Wei GW
• 通讯作者: Wei GW

AweGNN: Auto-parametrized weighted element-specific graph neural networks

AweGNN：自动参数化加权特定元素图神经网络

• 发表时间: 2022
• 期刊: Computers in biology and medicine
• 影响因子: 7.7
• 作者: Timothy Szocinski;Duc D Nguyen;Guo-Wei Wei
• 通讯作者: Guo-Wei Wei

Algebraic Graph-assisted Bidirectional Transformers for Molecular Prediction

• DOI: 10.21203/rs.3.rs-152856/v1
• 发表时间: 2021-01
• 作者: Dong Chen;Kaifu Gao;D. Nguyen;Xin Chen;Yi Jiang;G. Wei;F. Pan

Emerging Dominant SARS-CoV-2 Variants

• DOI: 10.1021/acs.jcim.2c01352
• 发表时间: 2023-01-09
• 期刊: JOURNAL OF CHEMICAL INFORMATION AND MODELING
• 影响因子: 5.6
• 作者: Chen, Jiahui;Wang, Rui;Wei, Guo-Wei
• 通讯作者: Wei, Guo-Wei

Biomolecular Topology: modelling and data analysis

生物分子拓扑：建模和数据分析

• 发表时间: 2022
• 期刊: Acta mathematica Sinica
• 作者: Jian Liu;Kelin Xia;Jie Wu;Stephen Yau;Guo-Wei Wei
• 通讯作者: Guo-Wei Wei