  Glycans are basic biomolecules and perform essential functions within living
organisms. The rapid increase of functional glycan data provides a good
opportunity for machine learning solutions to glycan understanding. However,
there still lacks a standard machine learning benchmark for glycan function
prediction. In this work, we fill this blank by building a comprehensive
benchmark for Glycan Machine Learning (GlycanML). The GlycanML benchmark
consists of diverse types of tasks including glycan taxonomy prediction, glycan
immunogenicity prediction, glycosylation type prediction, and protein-glycan
interaction prediction. Glycans can be represented by both sequences and graphs
in GlycanML, which enables us to extensively evaluate sequence-based models and
graph neural networks (GNNs) on benchmark tasks. Furthermore, by concurrently
performing eight glycan taxonomy prediction tasks, we introduce the
GlycanML-MTL testbed for multi-task learning (MTL) algorithms. Experimental
results show the superiority of modeling glycans with multi-relational GNNs,
and suitable MTL methods can further boost model performance. We provide all
datasets and source codes at https://github.com/GlycanML/GlycanML and maintain
a leaderboard at https://GlycanML.github.io/project
