DiffNMR2: NMR Guided Sampling Acquisition Through Diffusion Model Uncertainty

TL;DR

This paper introduces DiffNMR2, a diffusion model-based sampling method that significantly accelerates NMR spectral acquisition by guiding sampling with model uncertainty, improving accuracy and reducing time for complex biological samples.

Contribution

It presents a novel diffusion model approach for NMR sampling that outperforms existing strategies in accuracy and speed, enabling faster high-resolution spectral analysis.

Findings

Improves reconstruction accuracy by 52.9%.

Reduces hallucinated peaks by 55.6%.

Requires 60% less acquisition time.

Abstract

Nuclear Magnetic Resonance (NMR) spectrometry uses electro-frequency pulses to probe the resonance of a compound's nucleus, which is then analyzed to determine its structure. The acquisition time of high-resolution NMR spectra remains a significant bottleneck, especially for complex biological samples such as proteins. In this study, we propose a novel and efficient sub-sampling strategy based on a diffusion model trained on protein NMR data. Our method iteratively reconstructs under-sampled spectra while using model uncertainty to guide subsequent sampling, significantly reducing acquisition time. Compared to state-of-the-art strategies, our approach improves reconstruction accuracy by 52.9\%, reduces hallucinated peaks by 55.6%, and requires 60% less time in complex NMR experiments. This advancement holds promise for many applications, from drug discovery to materials science, where rapid and high-resolution spectral analysis is critical.