A transformer architecture for retention time prediction in liquid chromatography mass spectrometry-based proteomics

Thang V. Pham, Vinh V. Nguyen, Duong Vu, Alex A. Henneman, Robin A. Richardson, Sander R. Piersma, Connie R. Jimenez

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)


Accurate retention time (RT) prediction is important for spectral library-based analysis in data-independent acquisition mass spectrometry-based proteomics. The deep learning approach has demonstrated superior performance over traditional machine learning methods for this purpose. The transformer architecture is a recent development in deep learning that delivers state-of-the-art performance in many fields such as natural language processing, computer vision, and biology. We assess the performance of the transformer architecture for RT prediction using datasets from five deep learning models Prosit, DeepDIA, AutoRT, DeepPhospho, and AlphaPeptDeep. The experimental results on holdout datasets and independent datasets exhibit state-of-the-art performance of the transformer architecture. The software and evaluation datasets are publicly available for future development in the field.
Original languageEnglish
Article number2200041
Issue number7-8
Early online date2023
Publication statusPublished - Apr 2023


  • DIA-MS
  • deep learning
  • retention time prediction
  • spectral library
  • transformer architecture

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