Development of an in silico prediction model for chemical-induced urinary tract toxicity by using naïve Bayes classifier.

Abstract

The urinary tract toxicity is one of the major reasons for investigational drugs not coming into the market and even marketed drugs being restricted or withdrawn. The objectiveof this investigation is to develop an easily interpretable and practically applicable in silico prediction model of chemical-induced urinary tract toxicity by using naïve Bayes classifier. The genetic algorithm was used to select important molecular descriptors related to urinary tract toxicity, and the ECFP-6 fingerprint descriptors were applied to the urinary tract toxic/non-toxic fragments production. The established naïve Bayes classifier (NB-2) produced 87.3% overall accuracy of fivefold cross-validation for the training set and 84.2% for the external test set, which can be employed for the chemical-induced urinary tract toxicity assessment. Furthermore, six important molecular descriptors (e.g., number of N atoms, AlogP, molecular weight, number of H acceptors, number of H donors and molecular fractional polar surface area) and toxic and non-toxic fragments were obtained, which would help medicinal chemists interpret the mechanisms of urinary tract toxicity, and even provide theoretical guidance for hit and lead optimization.