Development of novel in silico prediction model for drug-induced ototoxicity by using naïve Bayes classifier approach. (June 2020)

• Record Type: Journal Article
• Authors: Zhang, Hui; Liu, Chun-Tao; Mao, Jun; Shen, Chen; Xie, Rui-Ling; Mu, Bo
• Is Part Of: Toxicology in vitro. Volume 65(2020)
• Publication Date: 2020-06
• Subjects: Ototoxicity -- In silico classification -- Naïve Bayes classifier -- Molecular descriptors -- Structural alerts; Toxicity testing -- In vitro -- Periodicals; Toxicology -- Periodicals; 615.9
• DOI: 10.1016/j.tiv.2020.104812
• Languages: English
• ISSNs: 0887-2333

Abstract

Abstract: 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. Graphical abstract: Unlabelled Image Highlights: A novel prediction model of ototoxicity was developed by using a naïve Bayes classifier. A set of 7 molecular descriptors considered as important for ototoxicity were identified. Some structural alerts for ototoxicity were identified.