# Towards Ultimate NMR Resolution with Deep Learning

arXiv: https://arxiv.org/abs/2502.20793 · HTML: https://mlsanity.com/paper/2502.20793/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2025.

## Abstract (arXiv)

In multidimensional NMR spectroscopy, practical resolution is defined as the ability to distinguish and accurately determine signal positions against a background of overlapping peaks, thermal noise, and spectral artifacts. In the pursuit of ultimate resolution, we introduce Peak Probability Presentations (P3)- a statistical spectral representation that assigns a probability to each spectral point, indicating the likelihood of a peak maximum occurring at that location. The mapping between the spectrum and P3 is achieved using MR-Ai, a physics-inspired deep learning neural network architecture, designed to handle multidimensional NMR spectra. Furthermore, we demonstrate that MR-Ai enables coprocessing of multiple spectra, facilitating direct information exchange between datasets. This feature significantly enhances spectral quality, particularly in cases of highly sparse sampling. Performance of MR-Ai and high value of the P3 are demonstrated on the synthetic data and spectra of Tau, MATL1, Calmodulin, and several other proteins.