Classification of Toxicity Effects of Biotransformed Hepatic Drugs Using Optimized Support Vector Machine

Abstract

Measuring toxicity is an important step in drug development, and there is a high demand to develop computational models that can predict the drug toxicity risks. In this study, we used a dataset that consists of 553 drug samples that biotransformed in liver. The toxic effects were calculated for the current data are mutagenic, tumorigenic, irritant, and reproductive effects. The proposed model has two phases, in the first phase; sampling algorithms were utilized to solve the problem of imbalanced dataset, in the second phase, the Support Vector Machines (SVM) classifier was used to classify an unknown drug sample into toxic or non-toxic. Moreover, in our model, Dragonfly Algorithm (DA) was used to optimize SVM parameters such as the penalty parameter and kernel parameters. The experimental results demonstrated that the proposed model obtained high sensitivity to all toxic effects, which indicates that it could be used for the prediction of drug toxicity in the early stage of drug development.