For a variety of reasons, most drug candidates cannot eventually pass the drug approval process. Thus, developing reliable methods for predicting clinical trial outcomes of drug candidates is crucial in improving the success rate of drug discovery and development. In this study, we propose an ensemble classifier based on weighted least squares support vector regression (LS-SVR) for predicting successes and failures of clinical trials. The efficacy of the proposed ensemble classifier is demonstrated through an experimental study on PrOCTOR dataset, which consists of informative chemical features of the drugs and target-based features. Comparing random forest and other models, the proposed ensemble classifier obtains the highest value for the area under the receiver operator curve (AUC). The results of this study demonstrate that the proposed ensemble classifier can be used to effectively predict drug approvals.
