Kernel-Based, Partial Least Squares Quantitative Structure-Retention Relationship Model for UPLC Retention Time Prediction: A Useful Tool for Metabolite Identification

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.