and suggest, as opposed to the regression models shortly described above, overall very good predictive performance. Comparing the results to the models of Alves et al. , previously trained on a subset of nearly 400 formulations, shows an overall similar performance; Acc, Sens, Spec and NPV values indicate good performance and are all ranging mostly between 0.7 and 0.9. In contrast, our models at higher thresholds, specifically LC30 and LC40, show smaller PPV values which also lead to lower F1 scores accordingly. This is an observation not detected by high AUC values (Figure ) which are often used as single evaluation metric. However, as our null models indicate (representing trivial classification models used as baseline estimation, assigning all instances to the positive class), a large data imbalance at these thresholds results in a very low prevalence of formulations exceeding these thresholds. In our extended formulation database, at a threshold of LC40 the amount of positive data points equals only about 5 % (see PPV value of LC40-NULL, Table ). Given a PPV CV statistic of 0.38 means that a selection based on the rdk5-LC40-RF model increases the chance of true positive hits up to 8-fold. Simultaneously high NPV values suggest that, despite these low PPV statistics, the models efficiently filter out large amounts of truly negative hits and could thus be used as a pre-screening tool in the early stages of drug formulation development, reducing the amount of compounds needed to be tested experimentally.