Abstract
Abstract
The near-infinite chemical diversity of natural and artificial macromolecules arises from the vast range of possible component monomers, linkages, and polymers topologies. This enormous variety contributes to the ubiquity and indispensability of macromolecules but hinders the development of general machine learning methods with macromolecules as input. To address this, we developed a chemistry-informed graph representation of macromolecules that enables quantifying structural similarity, and interpretable supervised learning for macromolecules. Our work enables quantitative chemistry-informed decision-making and iterative design in the macromolecular chemical space.
Funder
MIT-Takeda Fellowship
MIT Jameel Clinic
MIT Undergraduate Research Opportunity Fellowship
Novo Nordisk
Subject
Artificial Intelligence,Human-Computer Interaction,Software
Cited by
22 articles.
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