Transformer architectures in LC-MS/MS proteomics (scoping review)
review
Scoping review of Transformer and Transformer-hybrid models across de novo peptide sequencing and peptide property prediction. Searched PubMed/MEDLINE, Scopus and Web of Science for January 2017 to June 2026: 439 records, 207 after deduplication, 49 full texts assessed, 27 studies included, of which 13 were primarily de novo sequencing, 12 property prediction and 2 both. Finds encoder-decoder models dominant for spectrum-to-sequence generation and encoder-style or hybrid architectures common for fragment intensity, retention time, collision cross section and ion mobility. Its main argument is that reported gains are hard to compare at all, because benchmarks are heterogeneous, sequence-overlap auditing is incomplete, calibrated uncertainty is scarce and reporting is inconsistent.
| Kind | review |
Papers
- Transformer Architectures for De Novo Peptide Sequencing and Peptide Property Prediction in LC–MS/MS Proteomics (2026, Artificial Intelligence in the Life Sciences, peer-reviewed)