Enhancing generalization in non-uniformly sampled NMR spectra reconstruction via accelerated conditional diffusion models

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ID: 326337
2026
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Abstract
Abstract Multidimensional NMR spectroscopy provides rich molecular-level information on species and structures, with broad significance across chemistry, biology, and materials science. However, its widespread application is generally limited by prolonged acquisition times. Combining non-uniform sampling techniques with spectra reconstruction methods offers a promising solution to this acquisition bottleneck. Traditional reconstruction methods are robust but constrained by algorithmic assumptions and approximations, whereas deep learning approaches can potentially overcome these limitations and achieve higher reconstruction fidelity, though generalization to unseen data remains challenging. Here, we present an accelerated conditional diffusion model for multidimensional NMR spectra reconstruction, formulating the task as a probabilistic iterative denoising process that progressively refines undersampled spectra under physical constraints. Experiments demonstrate that this method outperforms both traditional and end-to-end deep learning algorithms in peak recovery, artifact suppression, and robustness across multiple sampling conditions and experimental datasets.
Reference Key
openalex_W7204101396 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Bo Chen, Xun Guan, Zhuoran Rong, Jingjie Lin, Ruoyin Lin, Yida Chen, 林恩平, Yu Yang, Yuqing Huang, Zhong Chen
Journal national science review
Year 2026
DOI
10.1093/nsr/nwag527
URL
https://doi.org/10.1093/nsr/nwag527
Keywords Keywords not found

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