Building of Robust and Interpretable QSAR Classification Models by Means of the Rivality Index

Abstract

An unambiguous algorithm, added to the study of the applicability domain and appropriate measures of the goodness of fit and robustness, represent the key characteristics that should be ideally fulfilled for a QSAR model to be considered for regulatory purposes. In this paper, we propose a new algorithm (RINH) based on the rivality index for the construction of QSAR classification models. This index is capable of predicting the activity of the data set molecules by means of a measurement of the rivality between their nearest neighbors belonging to different classes, contributing with a robust measurement of the reliability of the predictions. In order to demonstrate the goodness of the proposed algorithm we have selected four independent and orthogonally different benchmark data sets (balanced/unbalanced and high/low modelable) and we have compared the results with those obtained using 12 different machine learning algorithms. These results have been validated using 20 data sets of different balancing and sizes, corroborating that the proposed algorithm is able to generate highly accurate classification models and contribute with valuable measurements of the reliability of the predictions and the applicability domain of the built models.