Development of novel in silico prediction model for drug-induced ototoxicity by using naïve Bayes classifier approach.

Some drugs have the potential to cause cellular degeneration of cochlear and/or vestibular system, leading to temporary or permanent hearing loss, innitus, ataxia, dizziness, ear infections, hyperacusis, vertigo, nystagmus and other ear problems. Thus, precise assessment of ototoxicity has become a strong urge task for the toxicologist. In this research, the in silico prediction model of ototoxicity was developed based on 2612 diverse chemicals by using naïve Bayes classifier approach. A set of 7 molecular descriptors considered as important for ototoxicity was selected by genetic algorithm method, and some structural alerts for ototoxicity were identified. The established naïve Bayes prediction model produced 90.2% overall prediction accuracy for the training set and 88.7% for the external test set. We hope the established naïve Bayes prediction model should be employed as precise and convenient computational tool for assessing and screening the chemical-induced ototoxicity in drug development, and these important information of ototoxic chemical structures could provide theoretical guidance for hit and lead optimization in drug design.