用扩散模型融合多种谱图，自动推断有机分子结构。
DiSE: A diffusion probabilistic model for automatic structure elucidation of organic compounds
- 基于扩散模型联合解析质谱、核磁等多模态谱图数据。
- 在计算谱图上训练，仍能准确推断真实实验数据结构。
- 适合药物发现和自驱动实验室，提升自动化水平。
自动结构解析对自驱动实验室至关重要，它可实现闭环反馈，确保机器学习模型获得可靠的结构信息以支持实时决策与优化。本文提出DiSE，一种端到端的基于扩散的生成模型，整合质谱（MS）、¹³C和¹H化学位移、HSQC及COSY等多种光谱模态，实现有机化合物的自动化且高精度结构解析。通过数据驱动方式学习各光谱间的内在关联，DiSE在化学多样性数据集上表现出卓越准确性、强泛化能力，并对实验数据具有鲁棒性，尽管其训练仅使用计算谱图。DiSE为完全自动化结构解析带来重要进展，在天然产物研究、药物发现及自驱动实验室中具有广泛应用前景。
原文摘要 · Abstract (English)
Automatic structure elucidation is essential for self-driving laboratories as it enables the system to achieve truly autonomous. This capability closes the experimental feedback loop, ensuring that machine learning models receive reliable structure information for real-time decision-making and optimization. Herein, we present DiSE, an end-to-end diffusion-based generative model that integrates multiple spectroscopic modalities, including MS, 13C and 1H chemical shifts, HSQC, and COSY, to achieve automated yet accurate structure elucidation of organic compounds. By learning inherent correlations among spectra through data-driven approaches, DiSE achieves superior accuracy, strong generalization across chemically diverse datasets, and robustness to experimental data despite being trained on calculated spectra. DiSE thus represents a significant advance toward fully automated structure elucidation, with broad potential in natural product research, drug discovery, and self-driving laboratories.
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