Nuclear magnetic resonance (NMR) spectroscopy is an indispensable analytical technique in chemistry, biology, and medicine, offering molecular-level structural and dynamic insights for metabolite identification and biomarker discovery in biological and clinical research. However, its broader application in metabolite research is constrained by inherent limitations such as low sensitivity, severe peak overlap, and the trade-off between portability of low-field instruments and spectral resolution and sensitivity, as well as the difficulty of mining valid biological information from high-throughput metabolite big data. Recent advancements in artificial intelligence (AI), particularly deep learning (DL), have contributed to addressing these core challenges, reshaping the capabilities of NMR technology in metabolite research. This perspective overviews NMR’s core role and existing metabolite-specific bottlenecks, and traces the evolutionary progress of AI in NMR. Then it focuses on three interrelated and technologically innovative directions of AI-powered NMR for metabolite, including (1) improving the spectral resolution of low-field spectra to match that of high-field spectra, or directly extracting high-field-like NMR information from low-field spectra; (2) mitigating spectral congestion via optimizing pure shift spectra of complex metabolic mixtures, which simplifies complex spectra by converting multiplets into singlets; and (3) identifying metabolites and biomarkers (including lipoprotein-related metabolic markers in hyperlipidemia) in metabolomics. The synergy of AI and NMR is poised to unlock unprecedented analytical capabilities for metabolomics, extending its impact across scientific discovery, precision medicine, food science, and other related fields.
Lin et al. (2026) studied this question.