Development and evaluation of in silico prediction model for drug-induced respiratory toxicity by using naïve Bayes classifier method. (November 2018)

• Record Type: Journal Article
• Authors: Zhang, Hui; Ma, Jin-Xiang; Liu, Chun-Tao; Ren, Ji-Xia; Ding, Lan
• Is Part Of: Food and chemical toxicology. Volume 121(2018)
• Page Start: 593
• Page End: 603
• Publication Date: 2018-11
• Subjects: Respiratory toxicity -- In silico prediction -- Naïve Bayes classifier -- Genetic algorithm -- Extended connectivity fingerprints (ECFP_6); Toxicology -- Periodicals; Food poisoning -- Periodicals; Food Poisoning -- Periodicals; Toxicology -- Periodicals; Toxicologie -- Périodiques; Intoxications alimentaires -- Périodiques; Food poisoning; Toxicology; Periodicals; Electronic journals; 615.9
• DOI: 10.1016/j.fct.2018.09.051
• Languages: English
• ISSNs: 0278-6915

Abstract

Abstract: Respiratory toxicity is considered as main cause of drug withdrawal, which could seriously injure human health or even lead to death. The objective of this investigation was to develop an in silico prediction model of drug-induced respiratory toxicity by using naïve Bayes classifier. The genetic algorithm was used to select important molecular descriptors related to respiratory toxicity, and the ECFP_6 fingerprint descriptors were applied to the respiratory toxic/non-toxic fragments production. The established prediction model was validated by the internal 5-fold cross validation and external test set. The naïve Bayes classifier generated overall prediction accuracy of 91.8% for the training set and 84.3% for the external test set. Furthermore, six molecular descriptors ( e.g ., number of O atoms, number of N atoms, molecular weight, Apol, number of H acceptors and molecular polar surface area) considered as important for the drug-induced respiratory toxicity were identified, and some critical fragments related to the respiratory toxicity were achieved. We hope the established naïve Bayes prediction model could be used as a toxicological screening of chemicals for respiratory sensitization potential in drug development, and these obtained important information of respiratory toxic chemical structures could offer theoretical guidance for hit and lead optimization. Graphical abstract: Highlights: A novel prediction model of respiratory toxicity was developed by using a naïve Bayes classifier. Six molecular descriptors considered as important for respiratory toxicity were identified. Some critical fragments related to the respiratory toxicity were achieved.