Carbohydrates, vital components of biological systems, are well-known for their structural diversity. Nuclear Magnetic Resonance (NMR) spectroscopy plays a crucial role in understanding their intricate molecular arrangements and is essential in assessing and verifying the molecular structure of organic molecules. An important part of this process is to predict the NMR chemical shift from the molecular structure. This work introduces a novel approach that leverages E(3) equivariant graph neural networks to predict carbohydrate NMR spectra. Notably, our model achieves a substantial reduction in mean absolute error, up to threefold, compared to traditional models that rely solely on two-dimensional molecular structure. Even with limited data, the model excels, highlighting its robustness and generalization capabilities. The implications are far-reaching and go beyond an advanced understanding of carbohydrate structures and spectral interpretation. For example, it could accelerate research in pharmaceutical applications, biochemistry, and structural biology, offering a faster and more reliable analysis of molecular structures. Furthermore, our approach is a key step towards a new data-driven era in spectroscopy, potentially influencing spectroscopic techniques beyond NMR.

翻译：碳水化合物是生物系统的重要组成部分，以其结构多样性而闻名。核磁共振波谱在理解其复杂分子排列中起着关键作用，并且对于评估和验证有机分子的分子结构至关重要。该过程的一个重要环节是根据分子结构预测核磁共振化学位移。本研究提出了一种利用E(3)等变图神经网络预测碳水化合物核磁共振谱的新方法。值得注意的是，与仅依赖二维分子结构的传统模型相比，我们的模型实现了均方绝对误差的大幅降低，高达三倍之多。即使在数据有限的情况下，该模型也表现出色，凸显了其鲁棒性和泛化能力。其影响深远，不仅限于对碳水化合物结构和谱图解析的深入理解。例如，它可加速制药应用、生物化学和结构生物学领域的研究，提供更快、更可靠的分子结构分析。此外，我们的方法是迈向光谱学新数据驱动时代的关键一步，可能影响核磁共振以外的光谱技术。