The best models were used for a screening process with regard to known drug molecules. Compounds present within the DrugBank (bank.com/) were first selected based on the ALOGPS-derived solubility being below 10 g/L. This includes all (very) slightly soluble to practically insoluble drugs (as defined by the USP ). For the whole set of the remaining 9462 molecules, mixture descriptors were calculated, given hypothetical formulations with 5 different polymers present within the formulation database of this study, covering the most common B blocks (A-nPrOx-A , A-nPrOzi-A, A-nBuOx-A and A-nBuOzi-A ), as well as one aromatic B block type (A-BzOx-A ). For all of the 47310 theoretical formulations, polymer feeds of 10 g/L and drug feeds of 6 g/L with immediate solubilization measurement (0 days) were selected as experimental conditions. Predictions from all of the eight final classification models (LC10-40, LE20-80) were calculated and further assessed based on evaluation of the AD. ) and 0.56 (LE-RF model, Figure ). These statistics do not meet the recommended requirements for QSPR regression modelability and suggest that a classification approach with multiple thresholds, as performed by Alves et al. , should be preferred for this dataset. Thus, the following discussion will focus on the performance of the investigated classification tasks.