# Review and Prospect: Deep Learning in Nuclear Magnetic Resonance Spectroscopy

arXiv: https://arxiv.org/abs/2001.04813 · HTML: https://mlsanity.com/paper/2001.04813/

## Key facts

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

## Built on by (most cited first, 17 of 17)

- [Materials science in the era of large language models: a perspective](https://mlsanity.com/paper/2403.06949/) (2024)
- [Magnetic Resonance Spectroscopy Quantification Aided by Deep Estimations of Imperfection Factors and Macromolecular Signal](https://mlsanity.com/paper/2306.09681/) (2023)
- [A universal synthetic dataset for machine learning on spectroscopic data](https://mlsanity.com/paper/2206.06031/) (2022)
- [A Dynamics Theory of Implicit Regularization in Deep Low-Rank Matrix Factorization](https://mlsanity.com/paper/2212.14150/) (2022)
- [CloudBrain-NMR: An Intelligent Cloud Computing Platform for NMR Spectroscopy Processing, Reconstruction and Analysis](https://mlsanity.com/paper/2309.07178/) (2023)
- [Enhancing Peak Assignment in 13C NMR Spectroscopy: A Novel Approach Using Multimodal Alignment](https://mlsanity.com/paper/2311.13817/) (2023)
- [TVCondNet: A Conditional Denoising Neural Network for NMR Spectroscopy](https://mlsanity.com/paper/2405.11064/) (2024)
- [Reevaluating Convolutional Neural Networks for Spectral Analysis: A Focus on Raman Spectroscopy](https://mlsanity.com/paper/2509.25964/) (2025)
- [From Human Labels to Literature: Semi-Supervised Learning of NMR Chemical Shifts at Scale](https://mlsanity.com/paper/2601.18524/) (2026)
- [Magnetic Resonance Spectroscopy Deep Learning Denoising Using Few In Vivo Data](https://mlsanity.com/paper/2101.11442/) (2021)
- [GlycoNMR: Dataset and benchmarks for NMR chemical shift prediction of carbohydrates with graph neural networks](https://mlsanity.com/paper/2311.17134/) (2023)
- [Beyond traditional Magnetic Resonance processing with Artificial Intelligence](https://mlsanity.com/paper/2405.07657/) (2024)
- [An artificially intelligent magnetic resonance spectroscopy quantification method: Comparison between QNet and LCModel on the cloud computing platform CloudBrain-MRS](https://mlsanity.com/paper/2503.04469/) (2025)
- [Using machine learning to map simulated noisy and laser-limited multidimensional spectra to molecular electronic couplings](https://mlsanity.com/paper/2503.15706/) (2025)
- [Analyse comparative d'algorithmes de restauration en architecture d'epli'ee pour des signaux chromatographiques parcimonieux](https://mlsanity.com/paper/2510.18760/) (2025)
- [Quanvolutional Neural Networks for Spectrum Peak-Finding](https://mlsanity.com/paper/2512.13125/) (2025)
- [Data-driven Synthesis of Magnetic Resonance Spectroscopy Data using a Variational Autoencoder](https://mlsanity.com/paper/2603.00736/) (2026)

## 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.