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arXiv The work presents DiffGCMS, a spectrum-conditioned discrete graph diffusion model for de novo structure elucidation from GC-EI-MS, together with a two-stage framework in which DiffGCMS first generates candidate molecular structures from input spectra and a large language model then uses mass spectral information to validate, repair, and rerank the candidates while providing interpretable analysis of fragment-ion peaks; on a test set of 13,696 spectra from NIST 20 the generative model reached Acc@1 of 6.01% and Acc@10 of 15.76%, and on the subset of molecules with no more than 10 heavy atoms LLM-assisted molecular graph repair and reranking raised Acc@1 from 21.28% to 21.95%, Acc@10 from 46.91% to 47.99%, and candidate validity from 91.04% to 100%.
The work presents DiffGCMS, a spectrum-conditioned discrete graph diffusion model for de novo structure elucidation from GC-EI-MS, together with a two-stage framework in which DiffGCMS first generates candidate molecular structures from input spectra and a large language model then uses mass spectral information to validate, repair, and rerank the candidates while providing interpretable analysis of fragment-ion peaks; on a test set of 13,696 spectra from NIST 20 the generative model reached Acc@1 of 6.01% and Acc@10 of 15.76%, and on the subset of molecules with no more than 10 heavy atoms LLM-assisted molecular graph repair and reranking raised Acc@1 from 21.28% to 21.95%, Acc@10 from 46.91% to 47.99%, and candidate validity from 91.04% to 100%.
The work presents DiffGCMS, a spectrum-conditioned discrete graph diffusion model for de novo structure elucidation from GC-EI-MS, together with a two-stage framework in which DiffGCMS first generates candidate molecular structures from input spectra and a large language model then uses mass spectral information to validate, repair, and rerank the candidates while providing interpretable analysis of fragment-ion peaks; on a test set of 13,696 spectra from NIST 20 the generative model reached Acc@1 of 6.01% and Acc@10 of 15.76%, and on the subset of molecules with no more than 10 heavy atoms LLM-assisted molecular graph repair and reranking raised Acc@1 from 21.28% to 21.95%, Acc@10 from 46.91% to 47.99%, and candidate validity from 91.04% to 100%.
The work presents DiffGCMS, a spectrum-conditioned discrete graph diffusion model for de novo structure elucidation from GC-EI-MS, together with a two-stage framework in which DiffGCMS first generates candidate molecular structures from input spectra and a large language model then uses mass spectral information to validate, repair, and rerank the candidates while providing interpretable analysis of fragment-ion peaks; on a test set of 13,696 spectra from NIST 20 the generative model reached Acc@1 of 6.01% and Acc@10 of 15.76%, and on the subset of molecules with no more than 10 heavy atoms LLM-assisted molecular graph repair and reranking raised Acc@1 from 21.28% to 21.95%, Acc@10 from 46.91% to 47.99%, and candidate validity from 91.04% to 100%.