Chemistry-informed macromolecule graph representation for similarity computation, unsupervised and supervised learning

31Citations
Citations of this article
77Readers
Mendeley users who have this article in their library.

This article is free to access.

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.

Cite

CITATION STYLE

APA

Mohapatra, S., An, J., & Gómez-Bombarelli, R. (2022). Chemistry-informed macromolecule graph representation for similarity computation, unsupervised and supervised learning. Machine Learning: Science and Technology, 3(1). https://doi.org/10.1088/2632-2153/ac545e

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free