Chemistry-informed Macromolecule Graph Representation for Similarity Computation, Unsupervised and Supervised Learning

S Mohapatra, J An, R Gómez Bombarelli
2022 Mach. Learn.: Sci. Technol. 3 015028
Science Published: (Feb/2022)

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 method with macromolecules as input. To address this, we developed a chemistry-informed graph representation of macromolecules that enables quantifying structural similarity …

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