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

在科学学科的许多研究中，一个深刻的问题是什么形式承认所需的属性和行为。这个项目的重点是分子生物学的实例化。最近，公开可用的小分子数据库的快速增长引发了对硅内分子设计和优化的深度学习处理的许多研究和兴趣。虽然许多现有的深度学习方法展示了它们产生化学有效分子的能力，但它们目前在为旨在施加控制和回答以下问题的湿实验室研究提供信息方面的能力有限：你的信息学模型能否产生被限制在感兴趣的生物特性景观的这些特定区域的分子？模型、发现和数据将与科学界广泛传播。调查人员将联合指导各级学生。他们将他们的努力与他们机构的基础设施联系起来，以扩大他们的教育和外展活动的影响，并确保不同学科的学生参与到这个项目中来。这个项目促进了属性控制的分子生成。推动它的一个关键见解是，机器学习模型需要位于生物数据和知识中。研究活动分为三个方面：(1)开发能够纳入生物约束的可概括和可解释的模型；(2)容纳小的、不完整的和有噪声的湿实验室数据；以及(3)在主动学习公式下整合计算和湿实验室调查。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之间的协同和创新工作，以应对分子生物学中广泛存在的长期挑战，特别是季铵化合物(QAC)，这是一种小型消毒抗菌化合物，其结构创新严重缺乏，耐药细菌构成了无法抗衡的威胁。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更广泛的影响审查标准进行评估，被认为值得支持。

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.