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.<n>Our method iteratively reconstructs under-sampled spectra while using model uncertainty to guide subsequent sampling, significantly reducing acquisition time.<n>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: http://creativecommons.org/licenses/by/4.0/
• 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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