Aug 2026· Analytical Chemistry· Vol 98 34, pp.
25224-25232
· 0 citations· 33 references
Medicine
TL;DR
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.
Abstract
Phase-sensitive NMR spectroscopy provides essential information for accurate component identification, quantitative analysis, and structural characterization, particularly in protein studies. However, the acquisition of high-quality phase-sensitive NMR spectra with absorptive line shapes typically requires complementary quadrature acquisition and elaborate phase correction, which often involves additional experimental repetitions and time-consuming manual operations. In this study, we present 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. The proposed method formulates the phasing problem as a conditional probabilistic generative process in which a denoising network iteratively refines noisy spectral estimates toward physically consistent absorption-mode spectra under the guidance of the observed magnitude-mode data. Comprehensive validation on a diverse set of protein samples demonstrates the effectiveness and robustness of the proposed method, thus providing an effective and automated solution for phase-sensitive NMR spectroscopy reconstruction.
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
Pure shift methods greatly improve the resolution of nuclear magnetic resonance (NMR) spectra, aiding in the subsequent spectral analysis. However, existing approaches typically compromise sensitivity, introduce artifacts, or distort the signal integrals needed to quantify the amount of each component in a sample. Here, we propose a neural network, the Spin Echo to Pure Shift Network (SE2PSNet), that generates high-quality pure shift spectra with accurate integrals. Its input combines a set of spin echo spectra acquired at different echo times with a chemical shift binary spectrum produced by the existing SE2CSNet. From these, the network learns how NMR signals evolve across the spin echo spectra, while an attention mechanism with residual connections and a joint loss function amplify genuine signals, suppress noise, and extract integral-related features more accurately, thereby preserving both overall spectral quality and the weak signals essential for quantification. Across several representative samples, SE2PSNet resolved overlapping signals without introducing detectable artifacts, achieved sensitivity comparable to conventional single-pulse proton spectra, and provided accurate quantitative information. A neural network called SE2PSNet turns simple spin echo NMR data into high-quality pure shift spectra, resolving overlapping signals while preserving the sensitivity and accurate integral information, with broad potential applications.
MolDeTr addresses the spectrum-conditioned inverse problem and extracts spin-system parameters directly from measured 1D 1H NMR spectra, thereby substantially improving chemical-shift prediction precision by one to 2 orders of magnitude compared to existing structure-conditioned approaches.
N. Schmid, Marc Wanner, G. Fischetti et al.· Analytical Chemistry· 1 citation
Nuclear magnetic resonance (NMR) spectroscopy serves as a fundamental analytical technique finding valuable applications across chemistry, biology, and materials science, yet its widespread utility is frequently constrained by its inherently low sensitivity and concomitant noise, making noise reduction a critical processing step. Existing denoising methods typically impose a difficult trade-off, namely, traditional iterative algorithms are interpretable and widely adoptable but time-consuming, while deep learning approaches achieve fast denoising and better performance at the cost of generalizability and interpretability. To break this dilemma, we propose a denoising protocol, namely CCA-NMR, to unlock clean signals in noisy NMR spectra. This protocol exploits the idea of blind source separation to separate noise and real signals based on autocorrelation coefficients, delivering good noise reduction, fast computational time, and decent universality. Owing to these advantages, it enables reliable sensitivity benefits and recovers genuine peaks obscured by noise, thus reducing the necessary experimental acquisition times and facilitating subsequent quantitative analysis. Its performance is evidenced through experimental validation across a diverse range of NMR platforms, including relaxation and diffusion Laplace NMR, multidimensional protein NMR, and time-resolved NMR during real-time electrocatalytic reactions. As a consequence, these findings substantially expand the practical applicability of NMR spectroscopy, and indicate its considerable potential for broad chemical and biomedical applications.
Haolin Zhan, Yueyao Li, Yongqi Yuan et al.· Chemical Science· 0 citations
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