  In the dynamic field of nuclear magnetic resonance (NMR) spectroscopy,
artificial intelligence (AI) has ushered in a transformative era for molecular
studies. AI-driven NMR prediction, powered by advanced machine learning and
predictive algorithms, has fundamentally reshaped the interpretation of NMR
spectra. This innovation empowers us to forecast spectral patterns swiftly and
accurately across a broad spectrum of molecular structures. Furthermore, the
advent of generative modeling offers a groundbreaking approach, making it
feasible to make informed prediction of 2D NMR from chemical language (such as
SMILES, IUPAC Name). Our method mirrors the multifaceted nature of NMR imaging
experiments, producing 2D NMRs for the same molecule based on different
conditions, such as solvents and temperatures. Our methodology is versatile,
catering to both monosaccharide-derived small molecules, oligosaccharides and
large polysaccharides. A deeper exploration of the discrepancies in these
predictions can provide insights into the influence of elements such as
functional groups, repeating units, and the modification of the monomers on the
outcomes. Given the complex nature involved in the generation of 2D NMRs, our
objective is to fully leverage the potential of AI to enhance the precision,
efficiency, and comprehensibility of NMR spectral analysis, ultimately
advancing both the field of NMR spectroscopy and the broader realm of molecular
research.
