RepoRT: A comprehensive repository for small molecule retention times

05 July 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Liquid chromatography is frequently employed for the separation of metabolites and other small molecules. Prediction of retention times via machine learning methods can assist compound annotation. Yet, transferable predictions are intrinsically complicated for novel compounds and novel chromatographic conditions because retention times depend both on compound structure and the employed chromatographic system. We present RepoRT, the first repository for retention time data. RepoRT presently contains 373 datasets, 8809 unique compounds, and 88,325 retention time entries measured on 49 different chromatographic columns using varying eluents, flow rates, and temperatures. We put particular effort on making RepoRT “machine learning-ready”: We performed an extensive manual curation, cleaning and completion of the available data; we developed automated methods for data validation during upload; we collected more than 45,000 different columns of different vendors with their lengths, particle sizes, inner diameters, and pore sizes to create a database with normalized column names; and, we ensured that features required for transferable predictions, such as parameters numerically describing the selectivity of chromatographic columns, are readily available. For version control and reproducible research, RepoRT is hosted on GitHub.


retention time prediction
metabolite identification

Supplementary weblinks


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