用扩散模型不确定性指导采样，大幅缩短蛋白质NMR谱采集时间。
DiffNMR2: NMR Guided Sampling Acquisition Through Diffusion Model Uncertainty
- 基于扩散模型生成谱图，利用预测不确定性引导高效采样。
- 重建准确率提升52.9%，伪峰减少55.6%，采样时间节省60%。
- 适合需要快速高分辨谱图的药物研发与材料科学场景。
核磁共振（NMR）通过射频脉冲探测分子原子核的共振信号以解析结构，但高分辨率谱图的采集时间仍是瓶颈，尤其对复杂生物样品如蛋白质而言。本文提出一种新型高效子采样策略，基于在蛋白NMR数据上训练的扩散模型，通过迭代重建欠采样谱图，并利用模型不确定性引导后续采样，显著缩短采集时间。相比现有最优方法，本方法在复杂NMR实验中重建准确率提升52.9%，伪峰减少55.6%，采样时间减少60%。该技术在药物发现、材料科学等领域具有重要应用前景，可实现快速且高分辨率的谱图分析。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。