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 their ability to identify compounds absent from these libraries and to infer complete molecular structures directly from fragmentation information. Here, we present DiffGCMS, a spectrum-conditioned discrete graph diffusion model for de novo structure elucidation from GC--EI--MS, and further develop a framework that integrates DiffGCMS with second-stage reasoning by a large language model (LLM). In the first stage, DiffGCMS generates candidate molecular structures from input spectra; in the sec...
| Authors | Changlin Liu, Tianyu Yi, Chengchun Liu et al. |
|---|---|
| Primary category | physics.chem-ph |
| Published | 2 October 2026 |
| Last updated at source | 2 October 2026 |
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