GLAMOUR: Graph Learning over Macromolecule Representations
This addresses the problem of handling the vast chemical diversity of macromolecules for researchers in chemistry and materials science, representing an incremental advancement in domain-specific methods.
The authors tackled the challenge of developing general machine learning methods for macromolecules by creating GLAMOUR, a framework for chemistry-informed graph representations that enables structural similarity quantification and interpretable supervised learning.
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 GLAMOUR, a framework for chemistry-informed graph representation of macromolecules that enables quantifying structural similarity, and interpretable supervised learning for macromolecules.