Hugging Face Trending Papers October 8, 2026

MAST: Motif-Augmented Diffusion with Search Tree for Spectroscopic Molecular Structure Elucidation

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MAST is a motif‑augmented diffusion framework that uses a search tree to jointly infer 2D and 3D molecular structures from spectroscopic data. By incorporating explicit motif priors as intermediate evidence during denoising, it reduces spectral ambiguity and improves spectra‑conditioned optimization. The method casts diffusion sampling as a reward‑guided tree search, yielding a compact set of spectra‑consistent candidates with lower computational cost and achieving 94.89 % exact recovery on the QM9S benchmark while maintaining high chemical validity.

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arXiv AI

Oct 9

MAST: Motif-Augmented Diffusion with Search Tree for Spectroscopic Molecular Structure Elucidation

MAST is a motif‑augmented diffusion framework that uses a search tree to jointly elucidate 2D and 3D molecular structures from spectroscopic data. It incorporates explicit motif priors as intermediate evidence during denoising, reducing spectral ambiguity, and casts diffusion sampling as a reward‑guided tree search to prioritize high‑reward trajectories, thereby limiting computational overhead. On the QM9S multi‑spectra benchmark, MAST achieves 94.89% exact recovery, improves 3D fidelity, and maintains high chemical validity and stability.

By Chenghao Jia, Mengdi Liu, Hong Chang, Shiguang Shan, Xilin Chen

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Explainable Molecular Structure Inference from GC--MS with Diffusion Models and LLM Reranking

GC--EI--MS is an important technique for analyzing volatile and semivolatile compounds in complex samples. However, conventional methods rely heavily on reference spectral library matching, limiting t...

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MAST: Motif-Augmented Diffusion with Search Tree for Spectroscopic Molecular Structure Elucidation

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