Novel naïve Bayes classification models for predicting the chemical Ames mutagenicity

Abstract

Prediction of drug candidates for mutagenicity is a regulatory requirement since mutagenic compounds could pose a toxic risk to humans. The aim of this investigation was to develop a novel prediction model of mutagenicity by using a naïve Bayes classifier. The established model was validated by the internal 5-fold cross validation and external test sets. For comparison, the recursive partitioning classifier prediction model was also established and other various reported prediction models of mutagenicity were collected. Among these methods, the prediction performance of naïve Bayes classifier established here displayed very well and stable, which yielded average overall prediction accuracies for the internal 5-fold cross validation of the training set and external test set I set were 89.1±0.4% and 77.3±1.5%, respectively. The concordance of the external test set II with 446 marketed drugs was 90.9±0.3%. In addition, four simple molecular descriptors (e.g., Apol, No. of H donors, Num-Rings and Wiener) related to mutagenicity and five representative substructures of mutagens (e.g., aromatic nitro, hydroxyl amine, nitroso, aromatic amine and N-methyl-N-methylenemethanaminum) produced by ECFP_14 fingerprints were identified. We hope the established naïve Bayes prediction model can be applied to risk assessment processes; and the obtained important information of mutagenic chemicals can guide the design of chemical libraries for hit and lead optimization.