title: Kernel-Based, Partial Least Squares Quantitative Structure-Retention Relationship Model for UPLC Retention Time Prediction: A Useful Tool for Metabolite Identification.
abstract: We propose a new QSRR model based on a Kernel-based partial least-squares method for predicting UPLC retention times in reversed phase mode. The model was built using a combination of classical (physicochemical and topological) and nonclassical (fingerprints) molecular descriptors of 1383 compounds, encompassing different chemical classes and structures and their accurately measured retention time values. Following a random splitting of the data set into a training and a test set, we tested the ability of the model to predict the retention time of all the compounds. The best predicted/experimental R2value was higher than 0.86, while the best Q2value we observed was close to 0.84. A comparison of our model with traditional and simpler MLR and PLS regression models shows that KPLS better performs in term of correlation (R2), prediction (Q2), and support to MetID peak assignment. The KPLS model succeeded in two real-life MetID tasks by correctly predicting elution order of Phase I metabolites, including isomeric monohydroxylated compounds. We also show in this paper that the model's predictive power can be extended to different gradient profiles, by simple mathematical extrapolation using a known equation, thus offering very broad flexibility. Moreover, the current study includes a deep investigation of different types of chemical descriptors used to build the structure-retention relationship.
authors: Drug Discovery and Development Department, Fondazione Istituto Italiano di Tecnologia , Via Morego 30, 16163 Genova, Italy.
authors: Federico
authors: F
authors: Falchi
authors: Drug Discovery and Development Department, Fondazione Istituto Italiano di Tecnologia , Via Morego 30, 16163 Genova, Italy.
authors: Sine Mandrup
authors: SM
authors: Bertozzi
authors: Drug Discovery and Development Department, Fondazione Istituto Italiano di Tecnologia , Via Morego 30, 16163 Genova, Italy.
authors: Giuliana
authors: G
authors: Ottonello
authors: Drug Discovery and Development Department, Fondazione Istituto Italiano di Tecnologia , Via Morego 30, 16163 Genova, Italy.
authors: Gian Filippo
authors: GF
authors: Ruda
authors: Drug Discovery and Development Department, Fondazione Istituto Italiano di Tecnologia , Via Morego 30, 16163 Genova, Italy.
authors: Giampiero
authors: G
authors: Colombano
authors: Drug Discovery and Development Department, Fondazione Istituto Italiano di Tecnologia , Via Morego 30, 16163 Genova, Italy.
authors: Claudio
authors: C
authors: Fiorelli
authors: Drug Discovery and Development Department, Fondazione Istituto Italiano di Tecnologia , Via Morego 30, 16163 Genova, Italy.
authors: Cataldo
authors: C
authors: Martucci
authors: Drug Discovery and Development Department, Fondazione Istituto Italiano di Tecnologia , Via Morego 30, 16163 Genova, Italy.
authors: Rosalia
authors: R
authors: Bertorelli
authors: Drug Discovery and Development Department, Fondazione Istituto Italiano di Tecnologia , Via Morego 30, 16163 Genova, Italy.
authors: Rita
authors: R
authors: Scarpelli
authors: Drug Discovery and Development Department, Fondazione Istituto Italiano di Tecnologia , Via Morego 30, 16163 Genova, Italy.|Department of Pharmacy and Biotechnology, University of Bologna , Via Belmeloro 6, 40126 Bologna, Italy.
authors: Andrea
authors: A
authors: Cavalli
authors: Drug Discovery and Development Department, Fondazione Istituto Italiano di Tecnologia , Via Morego 30, 16163 Genova, Italy.
authors: Tiziano
authors: T
authors: Bandiera
authors: Drug Discovery and Development Department, Fondazione Istituto Italiano di Tecnologia , Via Morego 30, 16163 Genova, Italy.
authors: Andrea
authors: A
authors: Armirotti
mesh_terms: D000465:Algorithms; D002853:Chromatography, Liquid; D016018:Least-Squares Analysis; D008956:Models, Chemical; D025341:Principal Component Analysis