Multi-Agent Closed-Loop Reasoning for Organic Structure Elucidation from Multimodal Spectra
Authors: Bingsen Xue, Zhuojun Jiang, Jianhao Zhang, Mingcheng Gu, Yizhe Yuan, Yongtai Zhuo, Yifan Zhang, Li Wang, Ya Su, Yue Yuan, Jiang Liu, Xueqian Kong, Cheng Jin
Organizations: Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China · CAS Key Laboratory of Green Process and Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China · Department of Neurology, National Centre for Neurological Disorders, National Clinical Research Centre for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai 200240, China · Department of Microbiology Laboratory, Minhang District Center for Disease Control, Shanghai 200240, China · Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA · School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China · Beijing Anding Hospital Capital Medical University, Beijing, China · National Clinical Research Center for Kidney Diseases, Beijing, China · Institute of Digital Medicine, Shanghai Jiao Tong University, Shanghai, China
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.
School of AI for Science, Peking University Shenzhen Graduate School, Shenzhen 518055, China · State Key Laboratory of Advanced Waterproof Materials, School of Materials Science and Engineering, Peking University, Beijing 100871, China · School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen 518055, China +6
Information Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · The University of Hong Kong, Hong Kong, China · 3StateUniversity,Key Xiamen,LaboratoryChinaof Physical Chemistry of Solid Surface, College of Chemistry and Chemical Engineering, Xiamen +6
State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences, China · University of Chinese Academy of Sciences, China