Revisiting Cross-Validation of Drug Similarity Based Classifiers Using Paired Data

Abstract

Following the recent availability of high-throughput data for drug discovery, computational methods, especially machine learning based approaches, have gained remarkable attention. A number of studies use chemical, target and side effect similarity between drugs to build knowledge-based models that predict drug indications and drug-drug interactions. In light of previous works demonstrating the perils of cross-validation using paired data, in this study, we employ a disjoint cross validation approach for similarity-based drug-drug interaction (DDI) prediction and we investigate the prediction accuracy of classifier under various settings. Our results point to the dependence on the cross validation strategy used to evaluate prediction accuracy of drug similarity-based classifiers operating on paired data such as pharmacokinetic interactions between drugs.