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

现实世界中的许多系统和现象都可以用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。图通常用于编码这样的系统和现象，其中节点对应于实体，边对应于关系。图的计算分析多年来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，许多当前的图分析研究仅考虑图的拓扑（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，物理系统最精确地由几何图描述，也称为3D图，其中每个节点与3D物理空间中的坐标相关联。这种几何信息的精确编码在许多科学领域中是至关重要的。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关键取决于它们的3D形状，因为分子相互作用类似于锁和钥匙机制。该项目旨在通过开发可以准确有效地捕获图形的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来扩大对计算的参与。在该项目中，第一组研究任务旨在开发原则性3D图形神经网络，该网络可以使用小分子的3D几何信息的力量来生成信息丰富和有区别的表示。一个新的消息传递计划将开发一个完整的和有效的方式，将三维几何信息。在此基础上，将设计一种新的3D图形神经网络架构，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。现有的研究要么没有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的分层蛋白质图网络来学习不同层次的蛋白质表示。该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和高效的小分子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层次上实现可证明的完整性和效率。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

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.