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

• 批准号: 2318829
• 负责人: Amarda Shehu
• 金额: $ 29.93万
• 依托单位: George Mason 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=2318829&HistoricalAwards=false
• 关键词: Collaborative; Research; IIBR; Innovation; Bioinformatics; Collaborative; Research; IIBR; Innovation; Bioinformatics

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.

一个深刻的问题是跨科学学科进行大量询问的一个深刻的问题是，哪种形式承认所需的特性和行为。该项目的重点是分子生物学实例。公共可用的小分子数据库的快速增长产生了许多研究，最近在深度学习式分子设计和优化方面进行了有趣的研究。尽管许多现有的深度学习方法都表明了它们产生化学磁导体分子的能力，但目前，它们的能力限制了旨在执行控制和回答以下问题的湿生产研究的能力：您的信息模型可以生成受利益生物学特性的这些特定景观区域的限制的分子吗？模型，发现和数据将与科学界广泛传播。调查人员将共同指导各个级别的学生。他们将自己的努力与机构的基础设施联系起来，以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潜水员学生参与。该项目推动了财产控制的分子生成。一个关键的见解推动是机器学习模型需要位于生物学数据和知识中。研究活动分为三个推力：（1）开发能够纳入生物学约束的可推广且可解释的模型，（2）容纳小型，不完整和噪声湿可容易的数据，以及（3）在主动学习公式下整合计算和湿LAB查询。该项目在机器学习，AI，通用AI和生物学科学的界面上催化了协同和创新的工作，以应对分子生物学的长期挑战，既广泛，又专门针对季铵化合物（QACS）（QACS），BAC的baC，较小的疾病，均未抗拒抗药性的抗疾病，并均不在某种程度上不足的依赖性。反映了NSF的法定任务，并通过使用基金会的知识分子和更广泛的影响审查标准评估被认为是宝贵的支持。