arXiv cs.AI· Chenghao Jia, Mengdi Liu, Hong Chang, Shiguang Shan, Xilin Chen·· 9 天前AI 评分27
MAST：基于模体增强扩散与搜索树的波谱分子结构解析
MAST: Motif-Augmented Diffusion with Search Tree for Spectroscopic Molecular Structure Elucidation
AI 导读
MAST 是一个模体增强扩散框架，结合搜索树实现 2D-3D 联合波谱分子结构解析，在去噪过程中引入可解释的模体先验作为中间证据，并将扩散采样转化为奖励引导的树搜索。在 QM9S 多谱基准上，MAST 实现 94.89% 的精确恢复率，同时提升 3D 保真度并保持高化学有效性与稳定性。代码已开源。
正文 · 原文
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