Chemistry-informed macromolecule graph representation for similarity computation, unsupervised and supervised learning
Somesh Mohapatra, Joyce An, Rafael Gómez‐Bombarelli
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.
Topics & Concepts
MacromoleculeRepresentation (politics)Similarity (geometry)Computer scienceGraphChemistryArtificial intelligenceMachine learningTheoretical computer scienceBiochemistryImage (mathematics)Political scienceLawPoliticsMachine Learning in Materials ScienceComputational Drug Discovery MethodsAdvanced Polymer Synthesis and Characterization