The Better Predictive Model: High q for the Training Set or Low Root Mean Square Error of Prediction for the Test Set?

The process of validation of computational models (e.g., QSARs) may become the most important step in their development. Different requirements for the reliability and predictability of QSAR models have been described in the literature. Despite these formal recommendations there are few practical rules as to when to cease adding variables to a QSAR (i.e., what is an appropriate level of complexity of the model). In this work the influence of model complexity to statistical fit and error have been investigated using toxicity data for 200 phenols to the ciliated protozoan Tetrahymena pyriformis when applying a test set of a further 50 compounds. The results from this investigation showed that some important factors play a role in the definition of a good and reliable QSAR. These include the fact that q is not a good criterion for a model predictivity; that outliers should not necessarily be deleted as this may reduce the chemical space of the model; the number of descriptors in a multivariate model should be chosen carefully to avoid model underand over-estimation; and that an appropriate number of dimensions is required for PLS modelling.