We compared the consensus model and the single models of which it is composed using precision (positive predictive value -PPV), sensitivity (true positive rate -TPR), negative predictive value (NPV), and specificity (true negative rate -TNR), defined as: In order to estimate the applicability domain of the models, these metrics were computed on a distance-to-model (DM) basis. For that, the mean Jaccard distance from each compound in the test sets to all compounds in the training sets was calculated as the DM metric, using the three different fingerprints employed as molecular representation. These average distances were categorized in four quartiles considering all the cross-validated predictions, and all four metrics were calculated for each target and quartile, when predictions on the corresponding quartile were available. compared for the epigenetic target prediction problem. To assess the global performance of the combination of the single-target binary classifiers in the epigenetic target prediction task, ten samples of compounds containing the same number of active compounds for each target were assembled. To reduce the target-bias associated to the different sizes on the targetassociated compound datasets, each of the compound samples was constructed by iteratively sampling one compound labeled as "active" from the less populated dataset in the sample (or in alphabetical order according to its gene code when there was more than one less populated sample). This process was performed until the sample contained exactly 6 active compounds (20% of the active compounds for the smaller dataset) for each target. If the addition of a compound yields a target containing more than 6 active compounds, the compound was discarded, and if the equal number of active compounds for each target was not satisfied after 1000 iterations, 10% of the sample was randomly discarded, and the iterative sampling continued. These ten samples were used as validation sets so that compounds in the sample were removed from the original target-associated datasets. The single target binary classifiers were refitted using the hyperparameters selected in the Single Target Validation strategy. The performance for the combination of the single-target binary classifiers was assessed by its capability of identifying the known active compounds among the known compound target associations, using PPV and TPR as metrics in the same DM basis described in the Single Target Validation strategy.