Ensemble Technique for Toxicity Prediction of Small Drug Molecules of the Antioxidant Response Element Signalling Pathway

The in-silico toxicity prediction techniques are useful to reduce rodents testing (in-vivo). Authors have proposed a computational method (in silico) for the toxicity prediction of small drug molecules using their various physicochemical properties (molecular descriptors), which can bind to the antioxidant response elements (AREs). The software PaDEL-Descriptor is used for extracting the different features of drug molecules. The ARE data set has total 7439 drug molecules, of which 1147 are active and 6292 are inactive, and each drug molecule contains 1444 features. We have proposed a novel ensemble-based model that can efficiently classify active (binding) and inactive (non-binding) compounds of the data set. Initially, we performed feature selection using random forest importance algorithm in R, and subsequently, we have resolved the class imbalance issue by ensemble learning method itself, where we divided the data set into five data frames, which have an almost equal number of active and inactive drug molecules. An ensemble model based upon the votes of four base classifiers is proposed, which gives an accuracy of 97.14%. The K-fold cross-validation is conducted to measure the consistency of the proposed ensemble model. Finally, the proposed ensemble model is validated on some new drug molecules and compared with some existing models.