In general, molecules from the five commercial libraries exhibit distinguishable areas between all of them, both with MACCS Keys (Figure ) and ECFP4 (Figure ). Also, for this library, FooDB compounds are populating similar areas to those designed from DNMT1 inhibitors, unlike alvaBuilder molecules that share more space with UNPD-A compounds. In contrast, molecules designed from UNPD-A fragments are less spread than the alvaBuilder ones. This could be due to a reduction in the number of molecules for this data set. Notably, Selleckchem compounds have a tighter cluster than the rest of the databases, especially for the visualization with ECFP4 (Figure ). The distribution of the QED values indicates that the nine databases have lower QED medians than the reference database and, consequently, less drug-like properties (Figure ). Furthermore, the point distribution (one point represents one molecule) and the half-violin from the raincloud plot tend to have even lower QED values than the median. Since the visualization of chemical space based on drug-like properties indicated that these data sets populated similar areas, the decrease could be associated with the complexity penalty of QED. The increase in molecular complexity could be due to the characteristics of the binding cavity of DNMT1 since LigBuilder constructs the compounds in the binding site. Equally, the nine databases have less favorable scores regarding the distribution of SAscore values (Figure ). However, unlike the previous QED scores, most point distributions tend to cluster around the mean values. An increase in SAscore is expected and has been addressed as one of the issues with de novo design. Although the synthetic feasibility could be challenging, all the molecules are below the suggested threshold of six.