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

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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.