On the Use of the Metric rm2 as an Effective Tool for Validation of QSAR Models in Computational Drug Design and Predictive Toxicology

Validation of quantitative structure-activity relationship (QSAR) models plays a key role for the selection of robust and predictive models that may be employed for further activity prediction of new molecules. Traditionally, QSAR models are validated based on classical metrics for internal (Q(2)) and external validation (R-pred(2)). Recently, it has been shown that for data sets with wide range of the response variable, these traditional metrics tend to achieve high values without truly reflecting absolute differences between the observed and predicted response values, as in both cases the reference for comparison of the predicted residuals is the deviations of the observed values from the training set mean. Roy et al. have recently developed a new parameter, modified r(2) (r(m)(2)), which considers the actual difference between the observed and predicted response data without consideration of training set mean thereby serving as a more stringent measure for assessment of model predictivity compared to the traditional validation parameters (Q(2) and R-pred(2)). The r(m)(2) parameter has three different variants: (i) r(m(LOO))(2) for internal validation, (ii) r(m(test))(2) for external validation and (iii) r(m(overall))(2) for analyzing the overall performance of the developed model considering predictions for both internal and external validation sets. Thus, the r(m)(2) metrics strictly judge the ability of a QSAR model to predict the activity/toxicity of untested molecules. The present review provides a survey of the development of different r(m)(2) metrics followed by their applications in modeling studies for selection of the best QSAR models in different reports made by several workers.