A prediction model of drug-induced ototoxicity developed by an optimal support vector machine (SVM) method

• Published in: Computers in biology and medicine 2014-08, Vol.51, p.122-127
• Main Authors: Zhou, Shu, Li, Guo-Bo, Huang, Lu-Yi, Xie, Huan-Zhang, Zhao, Ying-Lan, Chen, Yu-Zong, Li, Lin-Li, Yang, Sheng-Yong
• Format: Article
• Language: English
• Subjects: Classification; Drug Discovery - instrumentation; Drug Discovery - methods; Drug-induced ototoxicity; Ears & hearing; Humans; Internal Medicine; Labyrinth Diseases - chemically induced; Labyrinth Diseases - metabolism; Methods; Models, Biological; Naïve Bayesian; Optimization algorithms; Other; Pharmaceuticals; Predictive Value of Tests; Recursive partitioning; Solvents; Support Vector Machine; Toxicity
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2014.05.005

Abstract Drug-induced ototoxicity, as a toxic side effect, is an important issue needed to be considered in drug discovery. Nevertheless, current experimental methods used to evaluate drug-induced ototoxicity are often time-consuming and expensive, indicating that they are not suitable for a large-scale evaluation of drug-induced ototoxicity in the early stage of drug discovery. We thus, in this investigation, established an effective computational prediction model of drug-induced ototoxicity using an optimal support vector machine (SVM) method, GA-CG-SVM. Three GA-CG-SVM models were developed based on three training sets containing agents bearing different risk levels of drug-induced ototoxicity. For comparison, models based on naïve Bayesian (NB) and recursive partitioning (RP) methods were also used on the same training sets. Among all the prediction models, the GA-CG-SVM model II showed the best performance, which offered prediction accuracies of 85.33% and 83.05% for two independent test sets, respectively. Overall, the good performance of the GA-CG-SVM model II indicates that it could be used for the prediction of drug-induced ototoxicity in the early stage of drug discovery.