Page "Quantitative structure–activity relationship" Paragraph 26

The success of any QSAR model depends on accuracy of the input data, selection of appropriate descriptors and statistical tools, and most importantly validation of the developed model.

Validation is the process by which the reliability and relevance of a procedure are established for a specific purpose ; for QSAR models validation must be mainly for robustness, prediction performances and applicability domain of the models.

Leave one-out cross-validation generally leads to an overestimation of predictive capacity, and even with external validation, no one can be sure whether the selection of training and test sets was manipulated to maximize the predictive capacity of the model being published.

Different aspects of validation of QSAR models that need attention includes methods of selection of training set compounds, setting training set size and impact of variable selection for training set models for determining the quality of prediction.

Development of novel validation parameters for judging quality of QSAR models is also important.