DiffNMR2: NMR Guided Sampling Acquisition Through Diffusion Model Uncertainty
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912388827054080 |
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| author | Goffinet, Etienne Yan, Sen Gabellieri, Fabrizio Jennings, Laurence Gkoura, Lydia Castiglione, Filippo Young, Ryan Malki, Idir Singh, Ankita Launey, Thomas |
| author_facet | Goffinet, Etienne Yan, Sen Gabellieri, Fabrizio Jennings, Laurence Gkoura, Lydia Castiglione, Filippo Young, Ryan Malki, Idir Singh, Ankita Launey, Thomas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_05230 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DiffNMR2: NMR Guided Sampling Acquisition Through Diffusion Model Uncertainty Goffinet, Etienne Yan, Sen Gabellieri, Fabrizio Jennings, Laurence Gkoura, Lydia Castiglione, Filippo Young, Ryan Malki, Idir Singh, Ankita Launey, Thomas Quantitative Methods Artificial Intelligence 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. |
| title | DiffNMR2: NMR Guided Sampling Acquisition Through Diffusion Model Uncertainty |
| topic | Quantitative Methods Artificial Intelligence |
| url | https://arxiv.org/abs/2502.05230 |