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广阔宇宙，每一种都有其独特的结构和功能。它们的数量如此之多（大约有一亿亿亿亿），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，这是一种擅长理解形成网络的数据的人工智能，比如分子之间如何相互作用。这些方法将根据每个分子的特定背景调整他们的理解，使它们变得通用和强大。通过使用这些方法收集数十亿的分子观察结果，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。这些引擎将能够快速有效地找到适合特定用途的最佳分子，通过同时考虑几十个因素，并发现以前仅通过实验室实验无法探索的新可能性。这可能会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，其中包括在本科和研究生阶段开发分子机器学习的新课程，并为学生准备人工智能驱动的科学角色。外联部分侧重于增加本科生，特别是女性和少数民族学生的研究参与，并教育他们在科学中负责任地使用人工智能。该项目开发基本的几何深度学习算法，用于分析治疗科学中的大型图结构数据集，重点是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。它的目标是探索广阔的分子空间，估计有10^60个分子，以及过多的蛋白质序列，以解锁具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。这些算法将具有上下文感知能力，能够根据它们所处的分子环境进行调整，并且具有足够的通用性，可以在有限的数据下推广到新的任务中。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。该项目将开拓通用图形表示的基础图形模型，这在分子机器学习中至关重要，为探索实验筛选无法实现的更大分子空间铺平道路，显著降低成本，并为治疗科学中的几何深度学习奠定基础。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

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.