Collaborative Research: IIBR: Innovation: Bioinformatics: Linking Chemical and Biological Space: Deep Learning and Experimentation for Property-Controlled Molecule Generation

合作研究：IIBR：创新：生物信息学：连接化学和生物空间：属性控制分子生成的深度学习和实验

基本信息

批准号：
2318831
负责人：
William Wuest
金额：
$ 31.68万
依托单位：
Emory University
依托单位国家：
美国
项目类别：
Continuing 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=2318831&HistoricalAwards=false
关键词：
Collaborative Research IIBR Innovation Bioinformatics

项目摘要

A profound question that underlies much inquiry across scientific disciplines is that of what forms admit desired properties and behaviors. The focus of this project is on a molecular biology instantiation. The rapid growth of publicly-available small molecular databases has spawned much research and interest recently in deep learning treatments of in-silico molecule design and optimization. While many of the existing deep learning methods demonstrate their ability to generate chemically-valid molecules, they are currently limited in their ability to inform wet-laboratory studies aiming to exert control and answer the following question: can your informatics model generate molecules that are constrained to these specific regions of a landscape of biological properties of interest? Models, findings, and data will be disseminated broadly with the scientific community. The investigators will jointly mentor students of all levels. They connect their efforts with their institution’s infrastructures to broaden the impact of their educational and outreach activities and ensure the participation of diverse students across the various disciplines that come together in this project.This project advances property-controlled molecule generation. A key insight propelling it is that machine learning models need to be situated in biological data and knowledge. The research activities are organized in three thrusts: (1) developing generalizable and interpretable models capable of incorporating biological constraints, (2) accommodating small, incomplete, and noisy wet-laboratory data, and (3) integrating computation and wet-lab inquiry under an active learning formulation. The project catalyzes synergistic and innovative work at the interface of machine learning, AI, generative AI, and the biological sciences to address long-standing challenges in molecular biology both broadly and specifically on quaternary ammonium compounds (QACs), small disinfectant antimicrobial compounds, where structural innovation has been sorely lacking and resistant bacteria represent an uncountered threat.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.

跨科学学科的许多探究背后的一个深刻问题是，什么形式可以实现所需的特性和行为，该项目的重点是分子生物学实例。最近，公开可用的小分子数据库的快速增长引发了许多研究和兴趣。虽然许多现有的深度学习方法证明了它们生成化学上有效的分子的能力，但它们目前在为旨在施加控制和答案的湿实验室研究提供信息方面受到限制。以下问题：可以您的信息学模型生成的分子仅限于感兴趣的生物特性的这些特定区域？模型、研究结果和数据将与科学界广泛传播，他们将与各个级别的学生联系起来。与其机构的基础设施一起扩大其教育和外展活动的影响，并确保该项目中各个学科的不同学生的参与。该项目推进了财产控制分子的生成，推动该项目的一个关键见解是机器学习。模型需要是研究活动分为三个重点：（1）开发能够纳入生物约束的可概括和可解释的模型，（2）适应小型、不完整和嘈杂的湿实验室数据，以及（3）整合。该项目促进了机器学习、人工智能、生成人工智能和生物科学领域的协同和创新工作，以解决分子生物学领域长期存在的广泛挑战。特别是在季铵化合物 (QAC) 和小型消毒抗菌化合物方面，这些化合物严重缺乏结构创新，耐药细菌构成了无法应对的威胁。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和能力进行评估，被认为值得支持。更广泛的影响审查标准。