Molecular representation learning (MRL) is a powerful tool for bridging the
gap between machine learning and chemical sciences, as it converts molecules
into numerical representations while preserving their chemical features. These
encoded representations serve as a foundation for various downstream
biochemical studies, including property prediction and drug design. MRL has had
great success with proteins and general biomolecule datasets. Yet, in the
growing sub-field of glycoscience (the study of carbohydrates, where longer
carbohydrates are also called glycans), MRL methods have been barely explored.
This under-exploration can be primarily attributed to the limited availability
of comprehensive and well-curated carbohydrate-specific datasets and a lack of
Machine learning (ML) pipelines specifically tailored to meet the unique
problems presented by carbohydrate data. Since interpreting and annotating
carbohydrate-specific data is generally more complicated than protein data,
domain experts are usually required to get involved. The existing MRL methods,
predominately optimized for proteins and small biomolecules, also cannot be
directly used in carbohydrate applications without special modifications. To
address this challenge, accelerate progress in glycoscience, and enrich the
data resources of the MRL community, we introduce GlycoNMR. GlycoNMR contains
two laboriously curated datasets with 2,609 carbohydrate structures and 211,543
annotated nuclear magnetic resonance (NMR) chemical shifts for precise
atomic-level prediction. We tailored carbohydrate-specific features and adapted
existing MRL models to tackle this problem effectively. For illustration, we
benchmark four modified MRL models on our new datasets.

分子表示学习（MRL）是一种将分子转化为数值表示并保留其化学特性的功能强大的工具，在机器学习和化学科学之间架起了桥梁。在糖科学领域，MRL 方法的探索相对较少，这主要归因于糖特定数据集的有限可用性和缺乏针对糖数据所提出的机器学习流程。为了解决这个挑战，推动糖科学的进展，丰富 MRL 社区的数据资源，我们引入了 GlycoNMR，其包含两个经过精心策划的数据集，共有 2,609 个糖结构和 211,543 个标注的核磁共振（NMR）化学位移以进行精确的原子级预测。我们针对这个问题定制了特定于糖的功能并改进了现有的 MRL 模型，在我们的新数据集上对四种修改后的 MRL 模型进行了基准测试。