{
  "abstract": "Here we present MassNet, inspired by large-scale data corpora of text and images; MassNet is an artificial intelligence (AI)-ready data corpus for mass spectrometry (MS)-based proteomics. The corpus contains 1.5 billion tandem MS spectra from high-quality data-dependent acquisition (DDA) MS data across 35 species. Approximately 1.7 million peptide precursors are from human samples. We further present an AI-ready DDA data format called Mass Spectrometry DDA Tensor (MSDT) for compact and standardized storage, curation and reuse of data from multiple sources, which can be optionally converted to a Lightning Memory-Mapped Database for accelerated input/output in deep learning. On the basis of MassNet, we develop XuanjiNovo, a nonautoregressive transformer model that leverages a curriculum learning strategy for de novo peptide sequencing. XuanjiNovo achieves 38.35–143.57% improvement over state-of-the-art methods on human Astral data. MassNet also supports additional downstream tasks, including retention time prediction and PSM rescoring. MassNet represents a foundational MS-based proteomics data resource, marking a critical milestone for AI proteomics research.",
  "abstract_shown": true,
  "affiliations": [
    "Affiliated Hangzhou First People’s Hospital, State Key Laboratory of Medical Proteomics, School of Medicine, Westlake University, Hangzhou, China",
    "Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China",
    "Research Center for Industries of the Future, School of Life Sciences, Westlake University, Hangzhou, China",
    "Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, China",
    "University of British Columbia, Vancouver, British Columbia Canada",
    "Shanghai Artificial Intelligence Laboratory, Shanghai, China",
    "College of Computer Science and Technology, Zhejiang University, Hangzhou, China",
    "Westlake Omics Biotechnology Co. Ltd, Hangzhou, China",
    "Suzhou Medical College, Soochow University, Suzhou, China",
    "Shanghai Innovation Institute, Shanghai, China"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 14,
  "authors": [
    {
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        0,
        1,
        2
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      "name": "Jun A"
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    {
      "affs": [
        3,
        4
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      "name": "Xiang Zhang",
      "orcid": "0009-0008-1928-5807"
    },
    {
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        0,
        1,
        2
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      "name": "Xiaofan Zhang"
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    {
      "affs": [
        5,
        6
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      "name": "Jiaqi Wei"
    },
    {
      "affs": [
        0,
        1,
        2
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      "name": "Te Zhang",
      "orcid": "0009-0003-8465-0501"
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    {
      "affs": [
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        1,
        2
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      "name": "Yamin Deng"
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    {
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        7
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      "name": "Pu Liu"
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    {
      "affs": [
        1
      ],
      "name": "Zongxiang Nie"
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    {
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        0,
        8
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      "name": "Yiwen Yu"
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    {
      "affs": [
        1
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      "name": "Yi Chen"
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    {
      "affs": [
        5,
        9
      ],
      "name": "Nanqing Dong",
      "orcid": "0000-0001-5014-1993"
    },
    {
      "affs": [
        5
      ],
      "name": "Zhiqiang Gao",
      "orcid": "0000-0001-8644-1541"
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    {
      "affs": [
        3,
        5
      ],
      "name": "Siqi Sun",
      "orcid": "0000-0001-7240-8724"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Tiannan Guo",
      "orcid": "0000-0003-3869-7651"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5hvfjwae0q15v.bib",
  "cited_by": 2,
  "doi": "10.1038/s41592-026-03247-y",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13736674/",
  "id": "5hvfjwae0q15v",
  "issns": [
    "1548-7091",
    "1548-7105"
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  "landing_url": "https://doi.org/10.1038/s41592-026-03247-y",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
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      "heading",
      "Article"
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      "openalex",
      "1607|Advanced Proteomics Techniques and Applications"
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  "pdf_url": "https://www.nature.com/articles/s41592-026-03247-y.pdf",
  "pmcid": "PMC13736674",
  "pmid": "42875595",
  "published": "2026-10-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5hvfjwae0q15v.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T12:22:04Z",
      "native_id": "PMC13736674",
      "source": "pmc",
      "url": "https://doi.org/10.1038/s41592-026-03247-y"
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  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:1607|Advanced Proteomics Techniques and Applications",
      "subfield": "physical"
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  ],
  "title": "MassNet: billion-scale AI-ready mass spectral corpus empowering scalable deep learning in proteomics",
  "title_text": "MassNet: billion-scale AI-ready mass spectral corpus empowering scalable deep learning in proteomics",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T12:22:04Z",
  "url": "https://research.draconitic.com/paper/5hvfjwae0q15v",
  "venue": "Nature Methods",
  "versions": []
}