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 R² value was higher than 0.86, while the best Q² value 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.