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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.
arXiv:2607. 23607v1 Announce Type: new Abstract: Molecular structure elucidation from tandem mass spectra (MS/MS) is a central inverse problem in analytical chemistry.
Nuclear Magnetic Resonance (NMR) spectroscopy is the gold standard for molecular structure elucidation, yet interpreting complex spectra for unknown molecules remains a bottleneck reliant on human expertise. While artificial intelligence has advanced this field, current methods face a critical trade-off: database retrieval cannot identify novel scaffolds, while de novo molecular structure elucidation models operate as black boxes, lacking the atom-level interpretability required for rigorous scientific validation.
arXiv:2604. 16648v2 Announce Type: replace Abstract: Tandem mass spectrometry is prominent in scientific discovery workflows for identifying unknown small molecules, yet high-throughput structural elucidation remains challenging.
arXiv:2608.30910v1 Announce Type: new Abstract: Spectroscopic structure elucidation is central to molecular analysis, but recent Large Language Model (LLM)-based methods mostly formulate it as direct...
Large language models (LLMs) are widely applied across chemical tasks, such as molecular property prediction, which underpins drug discovery. Molecular LLMs represent a molecule through several modalities, notably a 1D SMILES sequence or a 2D molecular graph.