Uncertainty-Aware Learning of Multiple Conditions as a Framework for Streamlined Retention Time Prediction to Accelerate Method Development
Year: 2026
Type: article
Abstract: Liquid chromatography is a cornerstone analytical technique for separating, quantifying, and purifying components from complex mixtures. To accelerate method screening, an uncertainty-aware graph-based neural network that predicts retention times across multiple column chemistries and buffer pH conditions is herein introduced. Specifically, the Multiple Condition Retention time model, MC-Retention, incorporates explicit column and buffer descriptors and is trained on a newly assembled dataset produced by screening 480 analytes under eight chromatography conditions. By being able to accurately model multiple chromatographic methods simultaneously, MC-Retention can substantially reduce the time and resources required for LC method screening and development. With an aggregate R2 of 0.86 and MAE of 15.5 seconds during cross validation, MC-Retention's respectable performance is greatly enhanced for analytes made present during training for one the four column chemistries when those analytes are otherwise not associated the columns being validated. This selective training of analytes for a single column chemistry reduces error by 50%, with an R2 of 0.95 and MAE of 8.2 seconds in aggregate. Additionally, we demonstrate that MC Retention effectively identifies optimal conditions for separating peptide coupling reaction components, while also suppling calibrated uncertainty estimates for its retention time predictions.
Source: Analytical Chemistry
Language: English
Subfield: Spectroscopy
Field: Chemistry
Domain: Physical Sciences
Open Access status: green
Funder: MSD Sharp and Dohme
OpenAlex ID: W7127158907