Accelerating multidimensional NMR acquisition underpins many advances in chemical and biomolecular research by enabling timely chemical insight, high-throughput spectral analysis, and access to transient or chemically evolving systems. Nonuniform sampling (NUS) offers a powerful route to accelerating multidimensional NMR experiments, but poses substantial challenges for accurate spectral reconstruction, especially in weak-peak regions. To overcome these limitations, we present a physics-guided deep learning framework, Consistency-guided Long-range Enhanced Attention for Reconstruction (CLEAR), which integrates convolutional layers with Transformer-based multi-head self-attention in a cascaded refinement architecture, enabling simultaneous modeling of local and global spectral correlations while enforcing strict data consistency with acquired samples. Comprehensive evaluations across multiple biomolecular NMR experiments demonstrate that CLEAR consistently outperforms state-of-the-art reconstruction methods, reducing reconstruction errors (RLNE) by approximately 16–25% while exhibiting overall superior or competitive performance across multiple quantitative metrics, including weak-peak preservation, under severe nonuniform sampling conditions (down to 5%). These results establish CLEAR as a robust and generalizable framework for high-fidelity NUS NMR spectral reconstruction.
Jing-Min Lin, Ze Fang, Bo Chen et al.· Analytical Chemistry· 0 citations
Diffusion Ordered NMR Spectroscopy (DOSY) is a powerful technique for studying mixtures by probing the diffusion behavior of mixed compounds and enabling their identification and separation in mixture samples. The performance of DOSY generally relies on the adopted reconstruction algorithm to determine diffusion coefficients from diffusion-dependent signal decays, thus producing a 2D spectrum that resolves components by chemical shift and molecular diffusion. Although deep-learning provides an effective approach to DOSY reconstruction, existing deep-learning-based reconstruction methods generally face the limitation of inadequate feature extraction, which may lead to reconstruction artifacts in some scenarios. Here, we propose STELT (Spatiotemporal Extraction Laplace Transform), a lightweight deep-learning framework based on spatiotemporal feature extraction. STELT uses a dual-branch architecture that combines a temporal convolution module to capture dynamic patterns in decay signals with a self-attention module to extract spatial features along the chemical-shift dimension. Experimental results demonstrate the proposed method achieves superior reconstruction accuracy and noise suppression with significantly reduced computational overhead, thereby offering a practical and efficient solution for high-quality DOSY analysis.
Jing-Min Lin, Bo Chen, Guolan Peng et al.· Journal of Chemical Physics· 0 citations
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.
Bo Chen, Xun Guan, Zhuoran Rong et al.· National Science Review· 0 citations
This study presents a diffusion-based deep-learning framework for automatic phase-sensitive NMR spectrum reconstruction directly from common NMR experimental data, free of quadrature acquisition and phase correction operation.
Zhuoran Rong, Bo Chen, Jie Shao et al.· Analytical Chemistry· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
