DiffNMR2: NMR Guided Sampling Acquisition Through Diffusion Model Uncertainty

• URL: http://arxiv.org/abs/2502.05230v1
• Date: Thu, 06 Feb 2025 20:10:28 GMT
• Title: DiffNMR2: NMR Guided Sampling Acquisition Through Diffusion Model Uncertainty
• Authors: Etienne Goffinet, Sen Yan, Fabrizio Gabellieri, Laurence Jennings, Lydia Gkoura, Filippo Castiglione, Ryan Young, Idir Malki, Ankita Singh, Thomas Launey,
• Abstract summary: We propose a novel 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. This advancement holds promise for many applications, from drug discovery to materials science, where rapid and high-resolution spectral analysis is critical.
• Score: 2.4634393035848494
• License:
• 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.

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