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Quantitative structure retention relationship (QSRR) modelling for Analytes’ retention prediction in LC-HRMS by applying different Machine Learning algorithms and evaluating their performance

Theodoros Liapikos, Ch. Zisi, Dritan Kodra, Katerina Kademoglou, Dimitra Diamantidou, Olga Begou, A. Pappa-Louisi, Georgios Theodoridis

2022Journal of Chromatography B47 citationsDOI

Topics & Concepts

CollinearitySupport vector machineLinear regressionArtificial intelligenceRegressionMolecular descriptorMachine learningChemistryRegression analysisCross-validationQuantitative structure–activity relationshipComputer scienceMathematicsStatisticsComputational Drug Discovery MethodsMetabolomics and Mass Spectrometry StudiesAnalytical Chemistry and Chromatography
Quantitative structure retention relationship (QSRR) modelling for Analytes’ retention prediction in LC-HRMS by applying different Machine Learning algorithms and evaluating their performance | Litcius