mlsanityData to 14 Oct 2026

DiffNMR2: NMR Guided Sampling Acquisition Through Diffusion Model Uncertainty

arXiv 2502.05230 ↗First posted 6 Feb 2025q-bio.QM cs.AIcited by 0

On the map: Diffusion models → Flow matching, time series and science

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 7 of its 26 references that are in the map. First posted on arXiv 6 Feb 2025.

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7 of 26 references.

References · 7

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  5. 2021LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairscited by 1,555
  6. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,430
  7. 2019Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learningcited by 16

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  1. 2025DiffNMR: Advancing Inpainting of Randomly Sampled Nuclear Magnetic Resonance Signals
  2. 2025DiffNMR3: Advancing NMR Resolution Beyond Instrumental Limits
  3. 2025DiffNMR: Diffusion Models for Nuclear Magnetic Resonance Spectra Elucidation
  4. 2025Atomic Diffusion Models for Small Molecule Structure Elucidation from NMR Spectra
  5. 2026MAST: Motif-Augmented Diffusion with Search Tree for Spectroscopic Molecular Structure Elucidation
  6. 2025DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra
  7. 2026Explainable Molecular Structure Inference from GC--MS with Diffusion Models and LLM Reranking
  8. 2025DiffSpectra: Molecular Structure Elucidation from Spectra using Diffusion Models