Classification of drugs in absorption classes using the classification and regression trees (CART) methodology

For the used dataset, CART models showed high descriptive and predictive abilities. The predictive abilities were evaluated based on both cross-validation and an external test set. Application of the variable ranking method to the models showed high importances for the n-octanol/water partition coefficient (log P) and polar surface area (PSA). This shows that CART is capable of selecting the most important descriptors, as known from the literature, for the absorption process in the intestinal tract.