MLR, PLSR‐BR Analysis and MBPLSR to Interpret Multivariate QSPR Models. The Case of a Micellar Liquid Chromatography Descriptor (log KWSDS)
Giuseppe Ermondi, Giulia Caron · Molecular Informatics · 2019
Abstract Improving the interpretability of multivariate QSPR models is a major issue in modern drug discovery. In this study we applied three strategies to model and deconvolute the balance of intermolecular forces governing log KWSDS, a chromatographic descriptor of potential relevance in the prediction of ADME phenomena. A dataset of 77 compounds was set‐up and an ad hoc pool of VS+ descriptors calculated. The data matrix was firstly submitted to a PCA run for a preliminary analysis and outliers detection. To model and interpret log KWSDS three chemoinformatic approaches implementing either variable selection or grouping tools were used: a) MLR and GA, b) PLSR combined with BR analysis and c) MBPLSR. Results provided by the three methods were largely superposable both in terms of prediction performances and mechanistic interpretation. Overall, they showed that log KWSDS is a complex descriptor mainly governed by the dimension, polarity and HBD solutes’ properties. Chemoinformatic strategies as those reported in this paper might be applied to any chromatographic system and thus represent a potent tool to exploit the full potential of chromatographic descriptors in pharmaceutical, toxicological and related sciences.