Developing novel in silico prediction models for assessing chemical reproductive toxicity using the naïve Bayes classifier method. Issue 9 (23rd March 2020)

• Record Type: Journal Article
• Authors: Zhang, Hui; Shen, Chen; Liu, Ru‐Zhuo; Mao, Jun; Liu, Chun‐Tao; Mu, Bo
• Is Part Of: Journal of applied toxicology. Volume 40:Issue 9(2020)
• Page Start: 1198
• Page End: 1209
• Publication Date: 2020-03-23
• Subjects: In silico prediction -- molecular descriptor -- Naïve Bayes classifier -- reproductive toxicity -- structural alerts; Toxicology -- Periodicals; Industrial toxicology -- Periodicals; Environmentally induced diseases -- Periodicals; Toxicology -- Periodicals; 615.9005
• DOI: 10.1002/jat.3975
• Languages: English
• ISSNs: 0260-437X

Abstract

Abstract: Assessment of reproductive toxicity is one of the important safety considerations in drug development. Thus, in the present research, the naïve Bayes (NB)‐classifier method was applied to develop binary classification models. Six important molecular descriptors for reproductive toxicity were selected by the genetic algorithm. Then, 110 classification models were developed using six molecular descriptors and10 types of fingerprints with 11 different maximum diameters. Among these established models, the model based on six molecular descriptors and the SciTegic extended‐connectivity fingerprints with 20 maximum diameters (LCFC_20) displayed the best prediction performance for reproductive toxicity (NB‐1), which gave a 0.884 receiver operating characteristic (ROC) score and 91.8% overall prediction accuracy for the Training Set, and produced a 0.888 ROC score and 83.0% overall accuracy for the external Test Set I. In addition, for the external rat multi‐generation reproductive toxicity dataset (Test Set II), the NB‐1 model generated a 0.806 ROC score and 85.1% concordance. The generated prediction results indicated that the NB‐1 model could give robust and reliable predictions for a reproductive toxicity potential of chemicals. Thus, the established model could be applied to filter early‐stage molecules for potential reproductive adverse effects. In addition, six important molecular descriptors and new structural alerts for reproductive toxicity were identified, which could help medicinal chemists rationally guide the optimization of lead compounds and select chemicals with the best prospects of being safe and effective. Abstract : Assessment of reproductive toxicity is one of the important safety considerations in drug development. In the present investigation, the naïve Bayes (NB)‐classifier method was applied to develop binary classification models for assessing reproductive toxicity. The best model (NB‐1) gave 91.8%, 83.0% and 85.1% overall prediction accuracies for the Training Set, Test Set I and rat multi‐generation reproductive toxicity dataset, respectively. In addition, six important molecular descriptors and new structural alerts for reproductive toxicity were identified.