DeepLC: Retention time prediction for peptides carrying any modification.
DeepLC predicts retention times for peptides carrying any modification. It does this by leveraging a deep learning model based on atomic composition features. Starting with v4, DeepLC comes with a multitask pretrained model covering multiple LC setups, enabling accurate predictions out of the box. For best results on a specific dataset, predictions can be calibrated or fine-tuned using a small reference set of identified PSMs.
If you use DeepLC, please cite:
DeepLC can predict retention times for peptides that carry as-yet unseen modifications
Robbin Bouwmeester, Ralf Gabriels, Niels Hulstaert, Lennart Martens & Sven Degroeve
Nature Methods 18, 1363–1369 (2021) doi:10.1038/s41592-021-01301-5
If you use the transfer learning functionality, please also cite:
Retention time prediction improves proteomics database search and identification rates
Nature Communications (2026) doi:10.1038/s41467-026-68981-5
To replicate the results from this paper, use DeepLC v3.1.13. For regular use, we recommend the latest stable version.
A hosted web application is available at iomics.ugent.be/deeplc — no installation required.
Windows: download the one-click installer from the releases page.
Other platforms: install with GUI dependencies and launch as a desktop app or local web server:
pip install deeplc[gui]
deeplc gui # opens in browser
deeplc gui --native # opens as desktop windowpip install deeplc
deeplc predict peptides.tsvfrom psm_utils.io import read_file
from deeplc import predict_and_calibrate
psm_list = read_file("peptides.tsv")
calibrated_rt = predict_and_calibrate(psm_list)See the documentation for the full CLI reference, Python API, and input file format.
- im2deep — Ion mobility / collisional cross section prediction using the same atomic composition approach
- MS²Rescore — Peptide identification rescoring that uses DeepLC retention time predictions as a rescoring feature
- iDeepLC — DeepLC variant using molecular descriptors to incorporate full molecular structure into predictions; better performance for some amino acid modifications.
Full documentation at deeplc.readthedocs.io:
- Usage — CLI, Python API, input formats
- Prediction models — Model descriptions and training data
- Migrating from v3 — API and format changes
- Changelog
- Contributing