Leon Niezen and Gert Desmet discuss uncertainty-driven LC method development, peak overlap, and moving from in silico to real data.
News|Videos|November 4, 2026
AI/ML Now: Uncertainty-Driven Model-Based Search Methods for Method Development in Liquid Chromatography (Part 1)
Leon Niezen and Gert Desmet describe how predictive uncertainty can make automated LC method development more reliable.
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In this episode of AI/ML Now, we talk with Leon Niezen and Gert Desmet of the Vrije Universiteit Brussel about how predictive uncertainty can make automated LC method development more reliable. In model-based method development, retention models are often refined by running each new experiment wherever the chromatographic response function (CRF) looks best. Niezen and Desmet took a different route: they gave the retention models a predictive uncertainty function and used the uncertainty itself to choose the next run.
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