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通过实体及其关系来描述。例如，社交网络由人和他们的关系组成，分子由通过化学键连接的原子组成。图通常用于对这样的系统和现象进行编码，其中节点对应于实体，边对应于关系。多年来，图的计算分析一直是一个活跃的研究领域，取得了大量丰硕的成果和发现。然而，当前许多图分析研究仅考虑图的拓扑（即这些关系的二维表示），而没有考虑重要的几何信息。在许多科学领域中，物理系统最准确地通过几何图（也称为 3D 图）来描述，其中每个节点都与 3D 物理空间中的坐标相关联。此类几何信息的准确编码在许多科学领域至关重要。例如，分子中的原子占据物理空间，它们的位置决定了 3D 分子几何形状。在药物发现中，药物的结合特性和有效性很大程度上取决于它们的 3D 形状，因为分子相互作用的作用类似于锁和钥匙机制。该项目旨在通过开发能够准确有效地捕获图形 3D 几何形状的算法来推进几何图形分析领域。该项目致力于通过吸引和激励 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，扩大对计算的参与。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，该网络可以利用小分子 3D 几何信息的力量来生成信息丰富且具有区分性的表示。将开发一种新颖的消息传递方案，以完整且有效的方式合并 3D 几何信息。在此开发的基础上，将设计一个新的 3D 图神经网络架构，以促进大规模分子数据的表示学习，并提高大量现实世界任务的性能和效率。第二组研究任务将所提出的完整且高效的 3D 图神经网络扩展到蛋白质的表示学习，蛋白质是具有根本重要性的复杂大分子。现有的研究要么未能考虑蛋白质中存在的层次关系，要么存在严重的效率问题。为了克服这些限制，本项目将开发一种新颖的分层蛋白质图网络来学习不同级别的蛋白质表示。所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。通过采用所提出的完整且高效的小分子 3D 图神经网络作为基础模型，新的分层蛋白质图网络有望在不同级别上实现可证明的完整性和效率。拟议的研究将产生供研究人员和从业人员使用的开源软件工具。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。