III: Small: 3D Graph Neural Networks: Completeness, Efficiency, and Applications

III：小：3D 图神经网络：完整性、效率和应用

• 批准号: 2243850
• 负责人: Shuiwang Ji
• 金额: $ 59.99万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2243850&HistoricalAwards=false
• 关键词: III; Small; 3D; Graph; Neural

Many real-world systems and phenomena can be described by entities and their relations. For example, social networks consist of people and their relationships, and molecules consist of atoms connected by chemical bonds. Graphs are commonly used to encode such systems and phenomena in which nodes correspond to entities, and edges correspond to relations. Computational analysis of graphs has been an active area of research for many years with a plethora of fruitful results and discoveries. However, many current graph analysis studies only consider topologies of graphs (i.e., a two-dimensional representations of these relationships), while important geometric information is not considered. In many scientific domains, physical systems are most accurately described by geometric graphs, also known as 3D graphs, in which each node is associated with a coordinate in 3D physical space. Accurate encoding of such geometric information is critical in many scientific domains. For example, atoms in a molecule occupy physical space, and their locations determine 3D molecular geometry. In drug discovery, the binding properties and thus effectiveness of drugs critically depend on their 3D shapes as molecular interactions act similarly to lock-and-key mechanisms. This project aims at advancing the field of geometric graph analysis by developing algorithms that can capture 3D geometries of graphs accurately and efficiently. The project is committed to broadening participation in computing by engaging and inspiring K-12 and underrepresented students in artificial intelligence and molecular analysis research and education. In this project, the first set of research tasks aim to develop principled 3D graph neural networks that can use the power of 3D geometric information of small molecules to generate informative and discriminative representations. A novel message passing scheme will be developed to incorporate 3D geometric information in a complete and efficient manner. Building on this development, a new 3D graph neural networks architecture will be designed to facilitate representation learning on large-scale molecule data and boost the performance and efficiency for a plethora of real-world tasks. The second set of research tasks extend the proposed complete and efficient 3D graph neural networks to representation learning of proteins, which are complex macromolecules of fundamental importance. Existing studies either fail to consider the hierarchical relations present in proteins or suffer from severe efficiency issues. To overcome these limitations, a novel hierarchical protein graph network to learn protein representations at different levels will be developed in this project. The proposed method faithfully integrates important hierarchical relations, resulting in a more natural protein learning scheme. By employing the proposed complete and efficient 3D graph neural networks for small molecules as a base model, the new hierarchical protein graph network is expected to achieve provable completeness and efficiency at different levels. The proposed research will result in open-source software tools to be used by researchers and practitioners.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.

许多现实世界的系统和现象可以由实体及其关系描述。例如，社交网络由人及其关系组成，分子由化学键相连的原子组成。图通常用于编码节点对应于实体的系统和现象，而边缘对应于关系。多年来，图形的计算分析一直是研究的积极研究领域，并且有很多富有成果的结果和发现。但是，许多当前的图形分析研究仅考虑图形的拓扑结构（即这些关系的二维表示），而不考虑重要的几何信息。在许多科学领域中，物理系统最精确地用几何图（也称为3D图）描述，其中每个节点与3D物理空间中的坐标相关联。在许多科学领域中，准确编码此类几何信息至关重要。例如，分子中的原子占据了物理空间，其位置决定了3D分子几何形状。在药物发现中，由于分子相互作用的作用类似于锁定机制，因此与药物的结合特性及其有效性取决于其3D形状。该项目旨在通过开发可以准确有效地捕获图形几何形状的算法来推进几何图分析领域。该项目致力于通过参与和启发K-12和代表性不足的学生参与人工智能和分子分析研究和教育来扩大计算的参与。在该项目中，第一组研究任务旨在开发有原则的3D图神经网络，这些神经网络可以利用小分子的3D几何信息的能力来产生信息和歧视性表示。将开发一种新的消息传递方案，以完整有效的方式合并3D几何信息。在此开发的基础上，将设计一个新的3D图神经网络体系结构，以促进对大型分子数据的代表性学习，并提高大量实际任务的性能和效率。第二组研究任务将所提出的完整有效的3D图神经网络扩展到表示蛋白质的表示，这些蛋白质是基本重要性的复杂大分子。现有的研究要么未考虑蛋白质中存在的分层关系，要么遭受严重效率问题。为了克服这些局限性，将在该项目中开发出一个新型的分层蛋白图网络，以学习不同级别的蛋白质表示。提出的方法忠实地整合了重要的层次关系，从而产生了更自然的蛋白质学习方案。通过使用小分子作为基本模型的建议完整有效的3D图神经网络，新的分层蛋白图网络有望在不同级别上实现可证明的完整性和效率。拟议的研究将导致研究人员和从业人员使用开源软件工具。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，认为值得通过评估来获得支持。