To ensure a decision tree model will perform well prospectively, compounds that were not present in the training set (i.e., test set), were evaluated using a bootstrapped set of models trained with different diagnostic branching ratio cutoffs. In addition, models were trained using different fingerprint radii to ensure that a radius of 1 is appropriate (see Introduction section for details). These 14 compounds (Table ) were selected from an in-house library of available compounds and the model was prospectively tested using diagnostic ionmolecule experiments. These 14 compounds (Table