  Macromolecules are large, complex molecules composed of covalently bonded
monomer units, existing in different stereochemical configurations and
topologies. As a result of such chemical diversity, representing, comparing,
and learning over macromolecules emerge as critical challenges. To address
this, we developed a macromolecule graph representation, with monomers and
bonds as nodes and edges, respectively. We captured the inherent chemistry of
the macromolecule by using molecular fingerprints for node and edge attributes.
For the first time, we demonstrated computation of chemical similarity between
2 macromolecules of varying chemistry and topology, using exact graph edit
distances and graph kernels. We also trained graph neural networks for a
variety of glycan classification tasks, achieving state-of-the-art results. Our
work has two-fold implications - it provides a general framework for
representation, comparison, and learning of macromolecules; and enables
quantitative chemistry-informed decision-making and iterative design in the
macromolecular chemical space.
