Unified Multitask Modeling for Retention Time Prediction Across Chromatographic Conditions

Retention time (RT) is a key parameter in liquid chromatography–mass
spectrometry (LC-MS) workflows, supporting compound identification,
feature alignment, and quality control. However, traditional RT prediction
models are built for specific chromatographic conditions, resulting
in fragmented knowledge and limited scalability. We introduce Uni-RT,
a unified multitask learning framework that simultaneously learns
from heterogeneous data sets to capture both shared molecular retention
patterns and condition-specific differences. By leveraging data across
multiple chromatographic setups, Uni-RT achieves higher accuracy and
robustness than pooled or condition-specific models while greatly
simplifying model deployment. Evaluation on 28 reversed-phase liquid
chromatography (RPLC) and hydrophilic interaction liquid chromatography
(HILIC) data sets demonstrates that multitask learning provides a
powerful and generalizable solution for integrating RT prediction
into diverse applications.