AI-enabled prediction of NMR spectroscopy: Deducing 2-D NMR of carbohydrate
<|reference_start|>AI-enabled prediction of NMR spectroscopy: Deducing 2-D NMR of carbohydrate: 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.<|reference_end|>
@article{li2024ai-enabled,
    title={AI-enabled prediction of NMR spectroscopy: Deducing 2-D NMR of
  carbohydrate},
    author={Yunrui Li, Hao Xu, Pengyu Hong},
    journal={arXiv preprint arXiv:2403.11353},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11353},
    primaryClass={cs.LG cs.AI physics.chem-ph}
}