Researchers Developed New Molecule Retention Tool
A new machine learning method now predicts molecular retention times in liquid chromatography experiments.
Updated on Oct. 5, 2026 in Chemistry
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Scientists from three German institutions created a machine learning model called 2-step to predict molecule retention times in reversed-phase liquid chromatography. The researchers released this method as both a software package and a web application.
Why it matters
Retention times vary significantly based on experimental variables like column type, pH, temperature, and solvent gradients, which complicates data analysis. This new tool offers a more consistent way to automate the prediction process.
The 2-step model works by calculating a retention order index for a molecule before converting that value into specific retention times using known reference points. This approach was published in the journal Nature Methods.
The players
Friedrich Schiller University Jena
This public research university located in Jena, Germany, served as a primary site for the development of the retention time prediction tool.
Helmholtz Zentrum München
This German research center focuses on environmental health and collaborated with other academic partners to develop the new computational method.
Technical University of Munich
This research university, consistently ranked among the top academic institutions in Europe, contributed to the collaborative development of the 2-step software.
The details
The method, which is specifically designed for the reversed-phase mode of liquid chromatography, has been made available to the scientific community as a web application and software package. It is intended to be integrated into existing analytical programs to streamline automated laboratory workflows.
Timeline
The research team published their method in the journal Nature Methods in October 2026.
The Big Picture
This development represents a significant paradigm shift toward fully automated laboratory data processing. By standardizing retention time predictions, the method bridges a critical gap in analytical chemistry workflows.
Integrating this tool into laboratory software could significantly reduce manual work for researchers performing chemical analysis. Future versions of this tool may eventually lead to faster and more reliable identification of unknown chemical compounds in diverse samples.
The takeaway
The implementation of machine learning in liquid chromatography highlights the ongoing shift toward automated analytical chemistry. Researchers using these tools can improve their experimental consistency by adopting computational predictions to complement empirical data.
Further reading
Learn more about the latest innovations in chemical research by exploring our Chemistry section.
Source note: This article includes information reported by Analytik.
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