mlsanityData to 14 Oct 2026

Review and Prospect: Deep Learning in Nuclear Magnetic Resonance Spectroscopy

arXiv 2001.04813 ↗First posted 13 Jan 2020physics.med-ph cs.LG eess.IVcited by 17

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Abstract (arXiv)

Since the concept of Deep Learning (DL) was formally proposed in 2006, it had a major impact on academic research and industry. Nowadays, DL provides an unprecedented way to analyze and process data with demonstrated great results in computer vision, medical imaging, natural language processing, etc. In this Minireview, we summarize applications of DL in Nuclear Magnetic Resonance (NMR) spectroscopy and outline a perspective for DL as entirely new approaches that are likely to transform NMR spectroscopy into a much more efficient and powerful technique in chemistry and life science.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Jan 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 4; 2024: 3; 2025: 5; 2026: 2.

17 papers.

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  1. 2024Materials science in the era of large language models: a perspectivecited by 24
  2. 2023Magnetic Resonance Spectroscopy Quantification Aided by Deep Estimations of Imperfection Factors and Macromolecular Signalcited by 6
  3. 2022A universal synthetic dataset for machine learning on spectroscopic datacited by 3
  4. 2022A Dynamics Theory of Implicit Regularization in Deep Low-Rank Matrix Factorizationcited by 1
  5. 2023CloudBrain-NMR: An Intelligent Cloud Computing Platform for NMR Spectroscopy Processing, Reconstruction and Analysiscited by 1
  6. 2023Enhancing Peak Assignment in 13C NMR Spectroscopy: A Novel Approach Using Multimodal Alignmentcited by 1
  7. 2024TVCondNet: A Conditional Denoising Neural Network for NMR Spectroscopycited by 1
  8. 2025Reevaluating Convolutional Neural Networks for Spectral Analysis: A Focus on Raman Spectroscopycited by 1
  9. 2026From Human Labels to Literature: Semi-Supervised Learning of NMR Chemical Shifts at Scalecited by 1
  10. 2021Magnetic Resonance Spectroscopy Deep Learning Denoising Using Few In Vivo Datacited by 0
  11. 2023GlycoNMR: Dataset and benchmarks for NMR chemical shift prediction of carbohydrates with graph neural networkscited by 0
  12. 2024Beyond traditional Magnetic Resonance processing with Artificial Intelligencecited by 0
5 more
  1. 2025An artificially intelligent magnetic resonance spectroscopy quantification method: Comparison between QNet and LCModel on the cloud computing platform CloudBrain-MRScited by 0
  2. 2025Using machine learning to map simulated noisy and laser-limited multidimensional spectra to molecular electronic couplingscited by 0
  3. 2025Analyse comparative d'algorithmes de restauration en architecture d'epli'ee pour des signaux chromatographiques parcimonieuxcited by 0
  4. 2025Quanvolutional Neural Networks for Spectrum Peak-Findingcited by 0
  5. 2026Data-driven Synthesis of Magnetic Resonance Spectroscopy Data using a Variational Autoencodercited by 0