Researchers develop AI tool to predict small molecule retention times
A team from Friedrich Schiller University Jena and partners in Munich presented a new AI method for predicting chemical retention times in Nature Methods.
New approach solves long-standing analytical problem
Researchers at Friedrich Schiller University Jena collaborated with institutions in Munich to address a decades-old issue in analytical chemistry. The team led by Prof. Dr. Sebastian Böcker created a tool that identifies small molecules within complex biological samples more reliably than previous methods.
Method improves identification of metabolites
Liquid chromatography separates substances based on how long they stay in a column before eluting. This retention time indicates which substance is present, but identifying small molecules like toxins or pharmaceuticals has remained difficult due to their high diversity.
Tool applies to drug discovery and environmental analysis
The new method supports work in drug discovery, environmental analysis, and metabolomics by providing accurate predictions for complex samples. It helps analysts distinguish between metabolic products, natural compounds, and degradation products found in blood or cell material.
Reported by one outlet
Only one outlet has published this. Nothing here has been checked against a second report, so read it as that outlet's account and follow the link below for the original.
Reported by
1 independent outlet. Headline as published. Links open the original report.