基于多智能体闭环推理的多模态光谱有机结构解析

中文摘要 AI 辅助

在分子发现与合成革命之后，从常规光谱数据进行可扩展的自动化结构解析仍是一项未解决的挑战。尽管经过数十年的计算努力，现有系统均无法对未知光谱实现可靠推理。本文提出MACROS，一种通过模拟专家迭代假设检验来实现结构解析自动化的多智能体系统。该系统在1亿个模拟光谱-分子对和160万个实验光谱-分子对上进行训练，原生支持常规光谱技术的任意组合。它实现了对各类真实世界样本前所未有的零样本泛化能力，使用一维NMR（核磁共振）正确识别分子量超过500 Da的合成化合物、天然产物和代谢物。值得注意的是，MACROS能从未分配数据中自发恢复教科书级光谱相关性，并表现出如优先解析环结构等涌现化学直觉，即学习基本化学原理而非记忆数据库模式。MACROS通过与化学家协作，使解析速度提升6倍、准确性提高40%，为全自动结构解析奠定了可扩展基础，并推动自主实验室加速分子发现。

英文摘要

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.

发表机构

• Shanghai Jiao Tong University（上海交通大学）
• Institute of Process Engineering, Chinese Academy of Sciences（中国科学院过程工程研究所）
• Huashan Hospital, Fudan University（复旦大学附属华山医院）
• Minhang District Center for Disease Control（闵行区疾病预防控制中心）
• Johns Hopkins University（约翰斯·霍普金斯大学）
• National Clinical Research Center for Kidney Diseases（国家肾脏疾病临床医学研究中心）

机构由 AI 辅助整理，请以论文原文为准。