Higher-Order Message Passing for Glycan Representation Learning

TL;DR

This paper introduces a novel higher-order message passing graph neural network architecture for glycan representation learning, significantly improving predictive performance on glycan property tasks.

Contribution

The work develops a new GNN model based on combinatorial complexes and higher-order message passing, achieving state-of-the-art results in glycan property prediction.

Findings

Achieved new state-of-the-art performance on GlycanML benchmark.

Demonstrated the effectiveness of higher-order message passing for complex biological graphs.

Enhanced glycan property prediction accuracy.

Abstract

Glycans are the most complex biological sequence, with monosaccharides forming extended, non-linear sequences. As post-translational modifications, they modulate protein structure, function, and interactions. Due to their diversity and complexity, predictive models of glycan properties and functions are still insufficient. Graph Neural Networks (GNNs) are deep learning models designed to process and analyze graph-structured data. These architectures leverage the connectivity and relational information in graphs to learn effective representations of nodes, edges, and entire graphs. Iteratively aggregating information from neighboring nodes, GNNs capture complex patterns within graph data, making them particularly well-suited for tasks such as link prediction or graph classification across domains. This work presents a new model architecture based on combinatorial complexes and higher-order message passing to extract features from glycan structures into a latent space representation. The architecture is evaluated on an improved GlycanML benchmark suite, establishing a new state-of-the-art performance. We envision that these improvements will spur further advances in computational glycosciences and reveal the roles of glycans in biology.