用AI从核磁谱图直接推断小分子结构，准确率超65%
Atomic Diffusion Models for Small Molecule Structure Elucidation from NMR Spectra
- 基于原子扩散模型与非等变Transformer，端到端生成分子结构
- 在11万种天然产物上训练，对复杂分子预测准确率超65%
- 适合药物研发、天然产物解析，加速分子发现进程
核磁共振（NMR）光谱是确定小分子结构的关键技术，尤其在新天然产物和临床药物发现中至关重要。然而，解析NMR谱图仍依赖耗时的人工分析和深厚的专业知识。我们提出ChefNMR（CHemical Elucidation From NMR），一种端到端框架，仅凭1D NMR谱图和化学式即可预测未知分子结构。将结构解析建模为基于非等变Transformer架构的原子扩散模型的条件生成任务。为模拟天然产物中的复杂化学基团，构建了超过111,000种天然产物的仿真1D NMR谱图数据集。ChefNMR在挑战性天然产物分子上的结构预测准确率超过65%，显著推进了小分子结构自动解析这一重大难题，并展示了深度学习在加速分子发现中的潜力。代码已开源：https://github.com/ml-struct-bio/chefnmr。
原文摘要 · Abstract (English)
Nuclear Magnetic Resonance (NMR) spectroscopy is a cornerstone technique for determining the structures of small molecules and is especially critical in the discovery of novel natural products and clinical therapeutics. Yet, interpreting NMR spectra remains a time-consuming, manual process requiring extensive domain expertise. We introduce ChefNMR (CHemical Elucidation From NMR), an end-to-end framework that directly predicts an unknown molecule's structure solely from its 1D NMR spectra and chemical formula. We frame structure elucidation as conditional generation from an atomic diffusion model built on a non-equivariant transformer architecture. To model the complex chemical groups found in natural products, we generated a dataset of simulated 1D NMR spectra for over 111,000 natural products. ChefNMR predicts the structures of challenging natural product compounds with an unsurpassed accuracy of over 65%. This work takes a significant step toward solving the grand challenge of automating small-molecule structure elucidation and highlights the potential of deep learning in accelerating molecular discovery. Code is available at https://github.com/ml-struct-bio/chefnmr.
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