QSAR classification of metabolic activation of chemicals into covalently reactive species

Abstract

Metabolic activation of chemicals into covalently reactive species might lead to toxicological consequences such as tissue necrosis, carcinogenicity, teratogenicity, or immune-mediated toxicities. Early prediction of this undesirable outcome can help in selecting candidates with increased chance of success, thus, reducing attrition at all stages of drug development. The ensemblemodelling ofmixed featureswas used for the development of amodel to classify themetabolic activation of chemicals into covalently reactive species. The effects of the quality of base classifiers and performance measure for sorting were examined. An ensemble model of 13 naive Bayes classifiers was built from a diverse set of 1,479 compounds. The ensemble model was validated internally with five-fold cross validation and it has achieved sensitivity of 67.4% and specificity of 93.4% when tested on the training set. The final ensemble model was made available for public use. © Springer Science+Business Media B.V. 2012.