Carbohydrate NMR chemical shift predictions using E(3) equivariant graph
  neural networks
Maria Bånkestad Keven M. Dorst Göran Widmalm Jerk Rönnols
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.