arXiv：cs.AI· Chenghao Jia, Mengdi Liu, Hong Chang, Shiguang Shan, Xilin Chen·· 4 小时前
MAST：用基元增强扩散与搜索树实现光谱分子结构解析
MAST: Motif-Augmented Diffusion with Search Tree for Spectroscopic Molecular Structure Elucidation
AI 导读
MAST 是一个基元增强扩散框架，结合搜索树实现 2D-3D 联合光谱分子结构解析，在 QM9S 多光谱基准上达到 94.89% 的精确恢复率并提升 3D 保真度。该方法在去噪过程中引入可解释的基元先验作为中间证据，缓解条件歧义，并将扩散采样建模为奖励引导的树搜索，在有限预算下筛选出紧凑的光谱一致候选集。代码已开源。
正文
Abstract: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 this https URL.
| Subjects: | Chemical Physics (physics.chem-ph); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.12067 [physics.chem-ph] |
| (or arXiv:2610.12067v1 [physics.chem-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12067 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Mengdi Liu [view email]
[v1]
Thu, 8 Oct 2026 14:45:56 UTC (2,227 KB)
来源：arXiv：cs.AI · arxiv.org