ABSTRACT
Quantitative structure–activity, structure–property, and structure–toxicity relationship (QSAR) models being used for the prediction of activity, property, and toxicity endpoints of untested chemicals can be exploited for the prioritization plan of synthesis and experimental testing as well as filling data gaps. In view of the importance of QSAR validation approaches and different validation parameters in the development of successful and acceptable QSAR models, this chapter focuses on error- as well as correlation-based metrics employed to judge the quality of the regression as well as classification-based QSAR models. Internal validation is performed with the compounds involved in the QSAR model development process. Reliability and acceptability of any QSAR model largely depend on the domain of applicability of molecules for computing the uncertainty in the prediction of a new chemical entity on the basis of similarity to the molecules utilized to construct the QSAR model.
