发表机构
Institute of Computing Technology, Chinese Academy of Sciences; University of Chinese Academy of Sciences（中国科学院计算技术研究所; 中国科学院大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
MAST是结合搜索树的基序增强扩散框架，用于联合2D-3D光谱分子结构解析，在QM9S基准上实现94.89%精确恢复率，提升3D保真度并降低计算开销。
AI 中文摘要
从光谱解析分子结构是化学与材料表征的基础问题，但因光谱歧义性及分子空间规模庞大而极具挑战性。尽管近期基于扩散的生成器在光谱条件下的结构解析中展现出强大潜力，现有方法仅依赖全局光谱表示，难以从有限的配对数据中学习鲁棒的光谱-结构关系；此外，重复的全采样推理策略会产生巨大的计算开销。为解决这些局限，我们提出MAST（Motif-Augmented diffusion with Search Tree，即结合搜索树的基序增强扩散框架），用于联合2D-3D光谱分子结构解析。MAST在去噪过程中引入显式、可解释的基序先验作为中间证据，降低条件歧义性并促进光谱条件优化；我们进一步将扩散采样转化为奖励引导的树搜索，以优先选择高奖励的去噪轨迹，在有限预算下生成一组与光谱一致的紧凑候选结构。在QM9S多光谱基准上，MAST实现了94.89%的精确恢复率，提升了3D保真度，同时保持了高化学有效性与稳定性。代码可在指定URL获取。
英文摘要
Elucidating molecular structures from spectra is a foundational problem in chemical and materials characterization, yet remains challenging due to spectral ambiguity and the vast molecular space. Although recent diffusion-based generators show strong promise for spectra-conditioned elucidation, existing methods struggle to learn robust spectra-structure relationships from limited paired data when relying solely on global spectral representation. Moreover, the repeated full sampling inference strategy incurs substantial computation overhead. To address these limitations, we propose \textbf{MAST}, a \textbf{M}otif-\textbf{A}ugmented diffusion framework with \textbf{S}earch \textbf{T}ree, for joint 2D-3D spectroscopic molecular structure elucidation. MAST introduces explicit, interpretable \emph{motif priors} as intermediate evidences throughout denoising, reducing conditional ambiguity and facilitating spectra-conditioned optimization. We further cast diffusion sampling as \emph{reward-guided tree search} to prioritize high-reward denoising trajectories, yielding a compact set of spectra-consistent candidates under limited budgets. On the QM9S multi-spectra benchmark, MAST achieves \textbf{94.89\%} exact recovery and improves 3D fidelity, while preserving high chemical validity and stability. Code is available at https://github.com/Jia040223/MAST.
