CAREER: Geometric Deep Learning to Facilitate Algorithmic and Scientific Advances in Therapeutics

想象一个由分子和蛋白质组成的巨大宇宙，每个分子和蛋白质都有其独特的结构和功能。它们有这么多（大约1万亿），它们都以复杂的方式相互作用。该项目将创建人工智能方法，以更好地理解和分析复杂的数据网络，特别是来自药物发现领域的数据。该项目将开发几何深度学习方法，这是一种人工智能，擅长理解形成网络的数据，例如分子如何相互作用。这些方法将根据每个分子的具体情况调整它们的理解，使它们变得通用和强大。通过使用这些方法聚合数十亿个分子观察结果，该项目将创建能够在各种标准中识别有用分子的分子搜索引擎。这些引擎将能够通过同时考虑数十个因素并发现以前仅通过实验室实验无法探索的新可能性，快速有效地找到用于特定目的的最佳分子。这可能导致新药被更快、更便宜地发现。该项目的一个组成部分是教育计划，其中包括为分子机器学习开发本科和研究生阶段的新课程，并为学生担任人工智能驱动的科学角色做好准备。外展部分的重点是增加本科生的研究参与，特别是女性和少数民族学生，并教育他们在科学中负责任地使用人工智能。该项目开发了基本的几何深度学习算法，用于分析治疗科学中的大型图形结构数据集，专注于聚合广泛的分子和蛋白质序列数据，以创建适应性强的分子搜索引擎。它的目标是探索巨大的分子空间，估计有10^60个分子，以及大量的蛋白质序列，以解开有治疗价值的分子相互作用。该项目的核心是开发创新的几何深度学习算法。这些算法将是上下文感知的，能够适应它们运行的分子环境，并且具有足够的通用性，可以推广到具有有限数据的新任务。他们将利用多模态信息为各种任务和领域生成适应性强的图形表示。该项目将开创通用图表示的基础图模型，这在分子机器学习中至关重要，为探索实验筛选无法获得的更大分子空间铺平道路，显着降低成本，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智力价值和更广泛的影响审查标准。

Imagine a vast universe of molecules and proteins, each with its unique structure and function. There are so many of them (around a trillion trillion trillion), and they all interact in complicated ways. This project will create artificial intelligence methods to better understand and analyze complex networks of data, especially those from the world of drug discovery. This project will develop geometric deep learning methods, a type of artificial intelligence that is good at understanding data that forms networks, such as how molecules interact with each other. These methods will adapt their understanding based on the specific context of each molecule, making them versatile and powerful. By aggregating billions of molecular observations using these methods, the project will create molecular search engines capable of identifying useful molecules across various criteria. These engines will be able to quickly and efficiently find the best molecules for specific purposes by considering dozens of factors all at once and discovering new possibilities that were previously impossible to explore just through experiments in a lab. This could lead to new drugs being discovered more quickly and cheaply. An integral part of the project is the education plan, which includes developing new curricula at undergraduate and graduate levels for molecular machine learning and preparing students for artificial intelligence-driven scientific roles. The outreach component focuses on increasing undergraduate research involvement, particularly among female and minority students, and educating them on the responsible use of AI in science. This project develops fundamental geometric deep learning algorithms for analyzing large, graph-structured datasets in therapeutic science, focusing on aggregating extensive molecular and protein sequence data to create adaptable molecular search engines. It aims to explore the vast molecular space, estimated at 10^60 molecules, and the plethora of protein sequences to unlock therapeutically valuable molecular interactions. The project's core is the development of innovative geometric deep learning algorithms. These algorithms will be context-aware, capable of adjusting to the molecular contexts in which they operate, and versatile enough to generalize to new tasks with limited data. They will leverage multimodal information to produce adaptable graph representations for various tasks and domains. This project will pioneer foundation graph models for general graph representations, crucial in molecular machine learning, paving the way to exploring larger molecular spaces inaccessible to experimental screening, significantly reducing costs, and establishing the foundation for geometric deep learning in therapeutic science.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.