{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,25]],"date-time":"2026-10-25T13:59:01Z","timestamp":1792936741508,"version":"4.5.0"},"reference-count":34,"publisher":"American Chemical Society (ACS)","issue":"16","license":[{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100021711","name":"Bayerisches Staatsministerium f?r Wissenschaft und Kunst","doi-asserted-by":"publisher","award":["Bavarian Research Network bayresq.net, project Met"],"award-info":[{"award-number":["Bavarian Research Network bayresq.net, project Met"]}],"id":[{"id":"10.13039\/501100021711","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/na","name":"German Research Council","doi-asserted-by":"publisher","award":["509149993 TRR 374"],"award-info":[{"award-number":["509149993 TRR 374"]}],"id":[{"id":"10.13039\/na","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,8,24]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Feature annotation in liquid chromatography\u2013mass spectrometry (LC\u2013MS)-based untargeted metabolomics remains challenging. Retention time (RT) prediction can support candidate prioritization and improve annotation confidence. Here, we present FastRet, an R package predicting RTs using Least Absolute Shrinkage and Selection Operator (LASSO) and Boosted Regression Trees (BRT) on molecular descriptors. FastRet provides a flexible framework combining from-scratch model training, selective measuring to prioritize metabolites for remeasurement, and model adjustment to adapt existing models to changed chromatographic conditions. Model training and prediction are completed within seconds on a single CPU core, and FastRet is accessible both from the R console and through a web interface. We validated FastRet on three in-house data sets covering reversed-phase chromatography (RP; N = 458), RP\u2013anion-exchange mixed-mode chromatography (RP-AXMM; N = 436), and hydrophilic interaction chromatography (HILIC; N = 388), plus one external HILIC data set from the Retip package (N = 970). Using a 2:1 training\/test split, BRT models trained from scratch achieved a test-set coefficient of determination (R2) of 0.86, 0.66, and 0.81 for the three in-house data sets. FastRet can also adjust a model to new chromatographic conditions from a few remeasured metabolites: using 25 RP metabolites measured under six modified conditions, adjustment reached R2 of 0.74 to 0.84 on unseen metabolites, a mean 0.22 gain over from-scratch models. Compared with published methods on identical splits, FastRet showed competitive performance for de novo prediction and superior performance in low-data transfer scenarios, while generalizing to 14 external data sets (median held-out R2 0.59). FastRet is available on CRAN with the web interface hosted at https:\/\/fastret.spang-lab.de.<\/jats:p>","DOI":"10.1021\/acs.jcim.6c01344","type":"journal-article","created":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T03:02:16Z","timestamp":1786590136000},"page":"10412-10425","source":"Crossref","is-referenced-by-count":1,"title":["FastRet: Fast and\nSimple Retention Time Prediction\nin Liquid Chromatography"],"prefix":"10.1021","volume":"66","author":[{"given":"Fadi","family":"Fadil","sequence":"first","affiliation":[{"name":"University of Regensburg , , ,","place":["Regensburg, Germany, 93053"]},{"name":"Immunology and Hygiene, University Hospital Cologne , , ,","place":["Ko\u0308ln, Germany, 50935"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tobias","family":"Schmidt","sequence":"additional","affiliation":[{"name":"University of Regensburg , , ,","place":["Regensburg, Germany, 93053"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Amesoeder","sequence":"additional","affiliation":[{"name":"University of Regensburg , , ,","place":["Regensburg, Germany, 93053"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simon","family":"Heckscher","sequence":"additional","affiliation":[{"name":"University of Regensburg , , ,","place":["Regensburg, Germany, 93053"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marian","family":"Schoen","sequence":"additional","affiliation":[{"name":"University of Regensburg , , ,","place":["Regensburg, Germany, 93053"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wolfram","family":"Gronwald","sequence":"additional","affiliation":[{"name":"University of Regensburg , , ,","place":["Regensburg, Germany, 93053"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter J.","family":"Oefner","sequence":"additional","affiliation":[{"name":"University of Regensburg , , ,","place":["Regensburg, Germany, 93053"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rainer","family":"Spang","sequence":"additional","affiliation":[{"name":"University of Regensburg , , ,","place":["Regensburg, Germany, 93053"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7337-2380","authenticated-orcid":true,"given":"Katja","family":"Dettmer","sequence":"additional","affiliation":[{"name":"University of Regensburg , , ,","place":["Regensburg, Germany, 93053"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,8,12]]},"reference":[{"key":"2026083104125424000_cit1","doi-asserted-by":"publisher","first-page":"1897","DOI":"10.1007\/s13361-016-1469-y","article-title":"Untargeted Metabolomics\nStrategies-Challenges and Emerging Directions","volume":"27","author":"Schrimpe-Rutledge","year":"2016","journal-title":"J. Am. Soc. Mass Spectrom."},{"key":"2026083104125424000_cit2","doi-asserted-by":"publisher","first-page":"12549","DOI":"10.1073\/pnas.1516878112","article-title":"Illuminating the\nDark Matter in Metabolomics","volume":"112","author":"da Silva","year":"2015","journal-title":"Proc. Natl. Acad.\nSci. U.S.A."},{"key":"2026083104125424000_cit3","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11306-007-0082-2","article-title":"Proposed Minimum Reporting Standards for Chemical Analysis\nChemical Analysis Working Group (CAWG) Metabolomics Standards Initiative\n(MSI)","volume":"3","author":"Sumner","year":"2007","journal-title":"Metabolomics"},{"key":"2026083104125424000_cit4","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1021\/acs.jproteome.1c00841","article-title":"Is Current\nPractice Adhering to Guidelines Proposed for Metabolite Identification\nin LC-MS Untargeted Metabolomics? A Meta-Analysis of the Literature","volume":"21","author":"Kodra","year":"2022","journal-title":"J. Proteome Res."},{"key":"2026083104125424000_cit5","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1021\/acs.analchem.0c04190","article-title":"Prediction\nof Analyte Retention Time\nin Liquid Chromatography","volume":"93","author":"Haddad","year":"2021","journal-title":"Anal. Chem."},{"key":"2026083104125424000_cit6","doi-asserted-by":"publisher","first-page":"1746","DOI":"10.1002\/jssc.202000060","article-title":"Current Status\nof Retention Time Prediction in Metabolite\nIdentification","volume":"43","author":"Witting","year":"2020","journal-title":"J. Sep. Sci."},{"key":"2026083104125424000_cit7","doi-asserted-by":"publisher","first-page":"3694","DOI":"10.1021\/acs.analchem.8b05820","article-title":"Comprehensive\nand Empirical Evaluation\nof Machine Learning Algorithms for Small Molecule LC Retention Time\nPrediction","volume":"91","author":"Bouwmeester","year":"2019","journal-title":"Anal. Chem."},{"key":"2026083104125424000_cit8","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-13680-7","article-title":"The METLIN Small Molecule Dataset\nfor Machine Learning-Based\nRetention Time Prediction","volume":"10","author":"Domingo-Almenara","year":"2019","journal-title":"Nat. Commun."},{"key":"2026083104125424000_cit9","doi-asserted-by":"publisher","first-page":"7515","DOI":"10.1021\/acs.analchem.9b05765","article-title":"Retip: Retention\nTime Prediction for Compound Annotation in Untargeted Metabolomics","volume":"92","author":"Bonini","year":"2020","journal-title":"Anal. Chem."},{"key":"2026083104125424000_cit10","doi-asserted-by":"publisher","DOI":"10.1016\/j.chroma.2025.465816","article-title":"RT-Pred: A web server\nfor accurate,\ncustomized liquid chromatography retention time prediction of chemicals","volume":"1747","author":"Zakir","year":"2025","journal-title":"J. Chromatogr. A"},{"key":"2026083104125424000_cit11","doi-asserted-by":"publisher","first-page":"7767","DOI":"10.1007\/s00216-020-02905-0","article-title":"Machine learning to predict retention\ntime of small molecules in nano-HPLC","volume":"412","author":"Osipenko","year":"2020","journal-title":"Anal.\nBioanal. Chem."},{"key":"2026083104125424000_cit12","doi-asserted-by":"publisher","DOI":"10.1016\/j.jchromb.2022.123132","article-title":"Quantitative Structure Retention Relationship (QSRR)\nModelling for Analytes\u2019 Retention Prediction in LC-HRMS by\nApplying Different Machine Learning Algorithms and Evaluating Their\nPerformance","volume":"1191","author":"Liapikos","year":"2022","journal-title":"J. Chromatogr. B"},{"key":"2026083104125424000_cit13","doi-asserted-by":"publisher","DOI":"10.1038\/s42004-024-01135-0","article-title":"Generic\nand accurate prediction of retention times in liquid chromatography\nby post-projection calibration","volume":"7","author":"Zhang","year":"2024","journal-title":"Commun. Chem."},{"key":"2026083104125424000_cit14","doi-asserted-by":"publisher","DOI":"10.1016\/j.chroma.2023.464439","article-title":"Deep graph\nconvolutional network for small-molecule\nretention time prediction","volume":"1711","author":"Kang","year":"2023","journal-title":"J. Chromatogr. 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Model."},{"key":"2026083104125424000_cit18","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.1021\/acs.jcim.4c02179","article-title":"ABCoRT: Retention\nTime\nPrediction for Metabolite Identification via Atom-Bond Co-Learning","volume":"65","author":"Cheng","year":"2025","journal-title":"J. Chem. Inf. Model."},{"key":"2026083104125424000_cit19","doi-asserted-by":"publisher","first-page":"7447","DOI":"10.1021\/acs.jcim.4c00608","article-title":"Transfer Learning Approach\nto Multitarget QSRR Modeling in RPLC","volume":"64","author":"Kumari","year":"2024","journal-title":"J. Chem.\nInf. Model."},{"key":"2026083104125424000_cit20","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1038\/s41592-023-02143-z","article-title":"RepoRT: a comprehensive\nrepository for small molecule\nretention times","volume":"21","author":"Kretschmer","year":"2024","journal-title":"Nat. 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R.\nStat. Soc. Ser. B Stat. Methodol."},{"key":"2026083104125424000_cit31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v033.i01","article-title":"Regularization Paths for Generalized\nLinear Models via Coordinate Descent","volume":"33","author":"Friedman","year":"2010","journal-title":"J. Stat.\nSoftware"},{"key":"2026083104125424000_cit32","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy\nFunction Approximation: A Gradient Boosting Machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"2026083104125424000_cit33","doi-asserted-by":"crossref","unstructured":"Chen, T.; Guestrin, C. In  XGBoost: A Scalable\nTree Boosting System, Proceedings of the 22nd ACM SIGKDD\nInternational Conference on Knowledge Discovery and Data Mining; ACM Digital Library, 2016; pp 785\u2013794.","DOI":"10.1145\/2939672.2939785"},{"key":"2026083104125424000_cit34","doi-asserted-by":"publisher","DOI":"10.1002\/9780470316801","volume-title":"Finding Groups in\nData: An Introduction to Cluster Analysis","author":"Kaufman","year":"1990"}],"container-title":["Journal of Chemical Information\nand Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jcim.6c01344","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/jcisd8\/article-pdf\/66\/16\/10412\/66521193\/acs.jcim.6c01344.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/pubs.acs.org\/jcisd8\/article-pdf\/66\/16\/10412\/66521193\/acs.jcim.6c01344.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T08:13:02Z","timestamp":1788163982000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/jcisd8\/article\/66\/16\/10412\/5254036\/FastRet-Fast-and-Simple-Retention-Time-Prediction"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,12]]},"references-count":34,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2026,8,12]]},"published-print":{"date-parts":[[2026,8,24]]}},"URL":"https:\/\/doi.org\/10.1021\/acs.jcim.6c01344","relation":{},"ISSN":["1549-9596","1549-960X"],"issn-type":[{"value":"1549-9596","type":"print"},{"value":"1549-960X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,12]]}}}