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The near-infinite chemical diversity of natural and artificial macromoleculesarises from the vast range of possible component monomers, linkages, andpolymers topologies. This enormous variety contributes to the ubiquity andindispensability of macromolecules but hinders the development of generalmachine learning methods with macromolecules as input. To address this, wedeveloped GLAMOUR, a framework for chemistry-informed graph representation ofmacromolecules that enables quantifying structural similarity, andinterpretable supervised learning for macromolecules.

GLAMOUR: Graph Learning over Macromolecule Representations