As a way of quantitatively evaluating whether the learned scores can be used to deprioritize compounds that could be seen as undesirable, we consider an approach similar to that one reported in the original QED study. Specifically, we scored different sets of molecules: a random subset of 30, 000 ChEMBL compounds present in the original pool for this study, a set of FDA-approved drugs as made available by the DrugBank database, and a random subset of 10, 000 compounds extracted from the GDB17 database. Furthermore, we used the latter GDB17 compounds as a control, since it was originally generated in a combinatorial fashion and should in practice also contain molecules that do not exhibit drug-like properties. Furthermore, to ensure that the molecules considered in these analyses did not fall out of the applicability domain of the trained model, we made sure to apply the same filtering procedures as those detailed in Section 2.3, resulting on 732, and 4889 analyzed molecules for the FDA-approved drugs and GDB sets, respectively. As a baseline method to compare the learned scores with, we considered the standard QED implementation as available on the RDKit package. On Figure , it can be observed that the distribution of learned scores is clearly well separated between sets better representing drug-like space (in other words those more apealing to medicinal chemists, i.e., Drugbank FDA-approved drugs and ChEMBL) against the GDB17 set. QED scores (Figure ) on the other hand, struggle at making such separation between the three sets. While an three-way ANOVA test was performed and the null hypothesis of equal mean values was rejected for both methods with virtually zero pvalues (F MolSkill = 945.69, F QED = 178), receiver operating characteristic curves to distinguish the druglike sets against GDB17 showed that only the proposed learned scores were predictive enough in practice for this task (Figures ).