A large-scale unified deep learning model for peptide mass spectrum interpretation trained on multimodal data

peer-reviewed · Nature Machine Intelligence · 2026

peer-reviewed · Nature Machine Intelligence · 2026. Jiale Zhao et al.
Date 2026-05-25
Type peer-reviewed
Venue Nature Machine Intelligence
Publisher Springer Nature
Contribution algorithm
DOI 10.1038/s42256-026-01234-8
Citations (OpenAlex) 1
Venue 2-year citedness 19.94

Authors

  1. Jiale Zhao · Chinese Academy of Sciences, University of Chinese Academy of Sciences
  2. Pengzhi Mao · Chinese Academy of Sciences, University of Chinese Academy of Sciences
  3. Kaifei Wang · Chinese Academy of Sciences, University of Chinese Academy of Sciences
  4. Yiming Li · University of Chinese Academy of Sciences
  5. Yaping Peng · Chinese Academy of Sciences, University of Chinese Academy of Sciences
  6. Ranfei Chen · Chinese Academy of Sciences, University of Chinese Academy of Sciences
  7. Shuqi Lu · DP Technology Co., Ltd.
  8. Xiaohong Ji · DP Technology Co., Ltd.
  9. Jiaxiang Ding · Chinese Academy of Sciences, University of Chinese Academy of Sciences
  10. Xin Zhang · University of Chinese Academy of Sciences
  11. Yucheng Liao · Peking University
  12. Weinan E · AI for Science Institute, Peking University
  13. Han Wen · AI for Science Institute, DP Technology Co., Ltd., State Key Laboratory of Medical Proteomics
  14. Weijie Zhang · DP Technology Co., Ltd.
  15. Hao Chi · Chinese Academy of Sciences, University of Chinese Academy of Sciences

Methods and tools

  • pUniFind: Multimodal pre-trained transformer for mass spectra that unifies peptide-spectrum scoring and zero-shot de novo sequencing in a single model. Trained on >100M open-search-derived spectra; reports +60% PSMs over prior de novo methods with 1,300+ modifications supported, and a DL-based QC step that recovers 38.5% additional peptides.

Cites (13)

Seen in the charts

Back to the full map

Back to top