{
  "abstract": "Mass spectrometry-based metabolomics increasingly relies on computational methods to translate information-rich MS/MS fragmentation patterns into chemical insight, yet assigning chemical identities to spectra (\"annotation\") remains a major bottleneck because reference libraries cover only a small fraction of plausible compounds. In this review, we frame the field through the lens of two interacting representation spaces: spectral space, where MS/MS data reside, and chemical space, where molecules are represented by fingerprints, descriptors, or learned embeddings. We discuss how different design choices induce different geometries, what it means for spectra or molecules to be \"close\", and survey methods for representing and comparing spectra, representing molecules, and mapping between both spaces. We highlight emerging deep-learning trends for cross-modal alignment and retrieval, and we outline where the analogy to text-image learning breaks due to peak ambiguity, noise, and the one-to-many nature of spectrum-to-structure relationships.",
  "abstract_shown": true,
  "affiliations": [
    "Centre for Digitalisation and Digitality",
    "Düsseldorf University of Applied Sciences",
    "Institute of Organic Chemistry and Biochemistry",
    "Czech Academy of Sciences",
    "Czech Institute of Informatics, Robotics and Cybernetics",
    "Czech Technical University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Julian Pollmann"
    },
    {
      "affs": [
        2,
        3,
        4,
        5
      ],
      "name": "Roman Bushuiev"
    },
    {
      "affs": [
        2,
        3,
        4,
        5
      ],
      "name": "Anton Bushuiev"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Tomáš Pluskal"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Florian Huber",
      "orcid": "0000-0002-3535-9406"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bmdjmpxkjxnvd.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15000536/v3",
  "fulltext_url": null,
  "id": "bmdjmpxkjxnvd",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15000536/v3",
  "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",
      "1312|Metabolomics and Mass Spectrometry Studies"
    ]
  ],
  "openalex": "W7212010601",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15000536/v3",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-09",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/bmdjmpxkjxnvd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:19Z",
      "native_id": "10.26434/chemrxiv.15000536",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15000536/v3"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Metabolomics and Mass Spectrometry Studies",
      "subfield": "molecular-biology"
    }
  ],
  "title": "Bridging MS2 Spectra and Chemical Space: Advances in Spectral Similarity, Molecular Retrieval, and De Novo Structure Discovery",
  "title_text": "Bridging MS2 Spectra and Chemical Space: Advances in Spectral Similarity, Molecular Retrieval, and De Novo Structure Discovery",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:19Z",
  "url": "https://research.draconitic.com/paper/bmdjmpxkjxnvd",
  "venue": "ChemRxiv",
  "versions": []
}