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.
Xiong et al. (2026) studied this question.