Multivariate prediction for QSAR

The biological activity of a chemical substance is often characterized by more than just one measured variable. In a QSAR (quantitative structure–activity relationships) context, this implies that one would like to predict several responses for given physico–chemical predictors. Reduced rank regression models are multivariate prediction methods which take advantage of the multivariate nature of the response and are easily interpretable. Prediction criteria for the selection of the rank, that is the dimensionality of the model, are discussed. Simulations and an example suggest that reduced rank regression can give better predictions than univariate methods or the multivariate competitor partial least squares.