Retention Time Dataset for Heterogeneous Molecules in Reversed–Phase LC

In a recent article published in Scientific Data (1), researchers provided details on the development of a comprehensive dataset comprising more than 10,000 experimental retention times, derived from 30 different reversed-phase liquid chromatography (LC) methods and pertaining to a collection of 343 small molecules representing a wide range of chemical structures. These chromatographic methods encompass common LC setups for studying the retention behavior of small molecules and offer a wide range of examples for modeling retention time with different LC setups.

LC-based separation aids in distinguishing isomeric and isobaric molecules, resulting in cleaner fragmentation spectra, and improves detection of low-abundance molecules by minimizing ionization competition (2), while chromatographic retention time (RT) provides crucial identification data, especially for molecules with indistinct mass or spectra but differing RTs (3). The use of RT in identification workflows, however, is often limited by the lack of reference standards as well as by the inconsistent RT across different chromatographic methods (CMs), thus affecting the availability of comprehensive datasets. Predicting RT for specific molecules within a given CM has become a popular alternative to (4-6) While in untargeted metabolomics, the use of quantitative structure−retention relationship (QSRR) strategies predicts RT for potential candidates, reducing false positives (7,8), past research has shown that the need for different QSRR models for different LC setups complicates this approach. (9-13).

To develop the database, researchers carefully selected the small molecules which made up their collection to represent various chemical classes and exhibit a wide range of physicochemical properties, effectively mimicking the diverse chemical space encountered in reverse–phase (RP) LC analyses. The CMs were then tailored to reflect common LC setups in untargeted analyses, incorporating different C18 columns, gradient profiles, mobile phases, and additives. The extensive molecular overlaps among the CMs in MCMRT make it easier to transfer machine learning models between different LC setups, thus enhancing their practical applicability in predicting RTs under various chromatographic conditions.

The authors stated that some of the molecules exhibited stable retention times across various mobile phases, while others displayed noticeable shifts, depending on the presence of specific additives such as formic acid or ammonium formate. This differentiation, they said, is crucial to understand the impact of chromatographic parameters on molecular retention and highlight the need for diverse experimental setups to capture a comprehensive range of retention behaviors. These findings emphasize the importance of their including a diverse array of molecules in the dataset to encompass multiple variations in RT and confirm the necessity of comprehensive datasets incorporating diverse molecular structures and chromatographic conditions to enhance the robustness and applicability of RT predictions.

Abstract molecule model. © Siarhei- stock.adobe.com

References

1. Zhang, Y.: Liu, F.; Li, X.Q. et al. Retention Time Dataset for Heterogeneous Molecules in Reversed–Phase Liquid Chromatography. Sci. Data 2024, 11, 946. DOI: 10.1038/s41597-024-03780-5

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