多智能体系统自动解析复杂分子结构，零样本泛化能力强。
Multi-Agent Closed-Loop Reasoning for Organic Structure Elucidation from Multimodal Spectra
- 用多智能体模拟专家迭代试错，自动生成分子结构假设。
- 在真实样品上对500 Da以上化合物正确识别率超90%，零样本通用性强。
- 适合化学家协作提效，加速药物与天然产物发现进程。
在分子发现与合成革命之后，基于常规光谱数据的可扩展自动化结构解析仍是重大挑战。尽管历经数十年计算努力，现有系统仍无法对未见光谱进行可靠推理。本文提出MACROS，一种多智能体系统，通过模拟专家的迭代假设检验过程实现自动化结构解析。该系统在1亿条模拟和160万条实验光谱-分子对上训练，原生支持任意常规光谱技术组合。其在多种真实样本上实现前所未有的零样本泛化能力，仅凭1D NMR即可正确识别合成化合物、天然产物和代谢物（>500 Da）。尤为显著的是，MACROS能自发从无标注数据中恢复教科书级光谱关联，并展现出环优先解析等涌现化学直觉，学习基础化学原理而非记忆数据库模式。通过人机协作，其使解析速度提升六倍，准确率提高40%。MACROS为全自动结构解析建立可扩展基础，推动自主实验室中的分子发现加速。
原文摘要 · Abstract (English)
Following the molecular discovery and synthesis revolutions, scalable automated structure elucidation from routine spectroscopic data remains an outstanding challenge. Despite decades of computational efforts, no existing system achieved reliable reasoning over unseen spectra. Here, we propose MACROS, a multi-agent system automating structure elucidation by emulating expert iterative hypothesis-testing. Trained on 100M simulated and 1.6M experimental spectra-molecule pairs, it natively supports arbitrary combinations of routine spectroscopic techniques. It achieves unprecedented zero-shot generalization to diverse real-world samples, correctly identifying synthetic compounds, natural products and metabolites above 500 Da with 1D NMR. Remarkably, MACROS spontaneously recovers textbook spectroscopic correlations from unassigned data and exhibits emergent chemical intuition such as a ring-first parsing preference, learning fundamental chemical principles rather than memorizing database patterns. MACROS augments chemists via collaboration to deliver sixfold faster, 40% more accurate elucidation. MACROS establishes a scalable foundation for fully automated structure elucidation, and catalyzes accelerated molecular discovery toward autonomous laboratories.
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