Discovery-Driven Mathematics and Artificial Intelligence for Biosciences and Drug Discovery

用于生物科学和药物发现的发现驱动数学和人工智能

• 批准号: 10551576
• 负责人: Guowei Wei
• 金额: $ 37.85万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-05 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10551576
• 关键词: 2019-nCoV; Address; Algorithms; Artificial Intelligence; Artificial Intelligence platform; Biological Sciences; Computer Assisted; Data; Directed Molecular Evolution; Drug Design; Evolution; Generations; Genome; Graph; Human; Mathematics; Modeling; Mutation; Play; Proteome; Recording of previous events; Research; Role; SARS-CoV-2 variant; Series; Site; Technology; Variant; Viral; algebraic topology; combinatorial; differential geometry; drug discovery; empowerment; innovation; laboratory experiment; pandemic disease; success; tool; transmission process

Discovery-driven mathematics and artificial intelligence for biosciences and drug discovery Project Summary Artificial intelligence (AI) is one of the most transformative technologies in human history and has profoundly changed the world around us in the past few years. Advancing AI has become a national strategy. Currently, AI is playing a crucial role in every aspect of biosciences. However, there are many challenges that that hinder the further advance of AI in pandemic forecasting, drug discovery, and directed evolution. My team has been addressing these challenges with a unique approach that utilizes advanced mathematics (i.e., algebraic topology, differential geometry, and combinatorial graphs) to empower AI for biosciences, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) modeling, drug discovery, and AI-assisted directed evolution. Our approach has had proven successes in discovering the mechanisms of SARS-CoV-2 evolution and transmission in the early stage of the pandemic (i.e., May 2020), successful forecasting of two key mutation sites involved in prevailing SARS-CoV-2 variants long before their occurrence, and in D3R Grand Challenges, a worldwide competition series in computer-aided drug design. I plan to further pursue this unique path by focusing on three ambitious directions: 1) Develop a genome-informed mathematical AI paradigm to predict emerging viral variants and their impacts; 2) Develop an automated, human-proteome informed AI platform for drug discovery, and 3) Develop a mathematical AI-assisted paradigm for directed evolution. My research will be carried out in strong partnerships with experimental labs, Pfizer, and Bristol Myers Squibb.

生物科学和药物领域的发现驱动数学和人工智能 发现 项目概要 人工智能（AI）是人类历史上最具变革性的技术之一， 在过去的几年里深刻地改变了我们周围的世界。推进人工智能已成为 国家战略。目前，人工智能在生物科学的各个方面都发挥着至关重要的作用。然而， 有许多挑战阻碍了人工智能在流行病预测方面的进一步发展， 药物发现和定向进化。我的团队一直在解决这些挑战 利用高等数学（即代数拓扑、微分 几何和组合图），为生物科学（包括重症急性疾病）提供人工智能支持 呼吸综合征冠状病毒 2 (SARS-CoV-2) 建模、药物发现和人工智能辅助 定向进化。我们的方法在发现机制方面已被证明是成功的 大流行早期（即2020年5月）的SARS-CoV-2进化和传播， 成功预测了与流行的 SARS-CoV-2 变体有关的两个关键突变位点 在其举办之前，以及在 D3R Grand Challenges 中，这是一项全球性的竞赛系列 计算机辅助药物设计。我计划通过关注三个方面来进一步追求这条独特的道路 雄心勃勃的方向：1）开发基于基因组的数学人工智能范式来预测 新出现的病毒变种及其影响； 2) 开发自动化的人类蛋白质组信息 用于药物发现的人工智能平台，以及 3) 开发用于定向药物发现的数学人工智能辅助范式 进化。我的研究将在与实验实验室、辉瑞、 和百时美施贵宝。