GLAMOUR: Graph Learning over Macromolecule Representations

TL;DR

GLAMOUR is a framework that uses chemistry-informed graph representations to quantify structural similarity and enable interpretable supervised learning for diverse macromolecules.

Contribution

It introduces a novel graph-based framework tailored for macromolecules, facilitating similarity measurement and interpretable machine learning in chemistry.

Findings

Enables quantification of macromolecular structural similarity.

Supports interpretable supervised learning for macromolecules.

Addresses the challenge of chemical diversity in machine learning applications.

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 GLAMOUR, a framework for chemistry-informed graph representation of macromolecules that enables quantifying structural similarity, and interpretable supervised learning for macromolecules.