AI method predicts retention times of small molecules more reliably
Researchers have developed a machine-learning method to predict the retention times of small molecules in liquid chromatography. This tool improves the identification of metabolites in complex biological samples, aiding fields like drug discovery.
Why it matters
Advancing analytical chemistry through AI allows for more efficient identification of toxins and pharmaceutical substances in biological research.
by Ute Schönfelder, Friedrich Schiller University of Jena
edited by Sadie Harley , reviewed by Robert Egan
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Add as preferred source The machine-learning model inputs a molecular structure and chromatographic condition parameters, and outputs a single number, the ROI. Compound structures are encoded using a D-MPNN, extra layers encode the chromatographic setup. FFN, feed-forward network. Credit: Nature Methods (2026). DOI: 10.1038/s41592-026-03243-2 Whether in drug discovery, environmental analysis or metabolomics: anyone analyzing complex biological samples often needs to identify the small molecules they contain. Researchers at Friedrich Schiller University Jena, in collaboration with partners from the Helmholtz Zentrum München and the Technical University of Munich, have developed a method that addresses a problem in analytical chemistry that has persisted for decades.
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