arXiv Machine Learning October 5, 2026 By Changlin Liu, Tianyu Yi, Chengchun Liu, Boxuan Zhao, Fanyang Mo

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

Read the original on arXiv Machine Learning →

The paper introduces DiffGCMS, a discrete graph diffusion model that generates molecular structures directly from GC–EI–MS spectra, and couples it with a large language model for post‑processing. In the first stage, DiffGCMS produces candidate structures; the LLM then validates, repairs, and reranks these candidates while offering interpretable explanations of fragment‑ion peaks. On a large NIST 20 test set, the combined approach improves accuracy and ensures 100% candidate validity for small molecules, demonstrating the value of spectrum‑aware post‑processing.

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