{
  "abstract": "Rapid and reliable structure elucidation remains a central bottleneck in automated reaction discovery, where highthroughput experimentation can generate large numbers of crude reaction mixtures faster than they can be interpreted. Gas chromatography coupled with mass spectrometry (GC-MS) provides rapid and information-rich analytical data, but identification of unknown reaction products still relies heavily on expert interpretation or spectral library matching, limiting applicability to novel compounds. Here we introduce Dynamic Mass-Aware Identification from Reaction Knowledge (DynaMAIK), a multimodal transformer framework for reaction-aware structure elucidation from GC-MS data. DynaMAIK combines electron ionization mass spectra with reaction context encoded as reactant and reagent SMILES to predict product structures and molecular formulas. Trained on more than three million reaction–spectrum pairs derived from curated reaction databases, spectral simulation and experimental spectra, DynaMAIK achieved 87% top-1 and 94% top-10 structure accuracy on a held-out experimental test set. Comparisons with spectrum-only and reaction-only models, together with ablation and scrambling experiments, show that spectral evidence and reaction-derived context provide complementary constraints for product identification. Application to high-throughput thiolation reactions demonstrates accurate product assignment directly from experimental screening data. DynaMAIK establishes context-aware GC-MS interpretation as a scalable route toward automated analytical feedback in reaction discovery workflows.",
  "abstract_shown": true,
  "affiliations": [
    "Organisch-Chemisches Institut",
    "Universität Münster",
    "NXT Spectra, ChemInnovation GmbH"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Maik G. Niedziella"
    },
    {
      "affs": [
        2
      ],
      "name": "Philipp M. Pflüger"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Ajnabiul Hoque"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Frank Glorius",
      "orcid": "0000-0002-0648-956X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8asenbsnyg5jw.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15008991/v1",
  "fulltext_url": null,
  "id": "8asenbsnyg5jw",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008991/v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1607|Mass Spectrometry Techniques and Applications"
    ]
  ],
  "openalex": "W7213441566",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15008991/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-17",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/8asenbsnyg5jw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:19Z",
      "native_id": "10.26434/chemrxiv.15008991",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008991/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:1607|Mass Spectrometry Techniques and Applications",
      "subfield": "physical"
    }
  ],
  "title": "Chemistry-Informed Multimodal Model for Structure Elucidation in Automated Reaction Discovery",
  "title_text": "Chemistry-Informed Multimodal Model for Structure Elucidation in Automated Reaction Discovery",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:19Z",
  "url": "https://research.draconitic.com/paper/8asenbsnyg5jw",
  "venue": "ChemRxiv",
  "versions": []
}