Moreover, 159 compounds obtained mean Jaccard distances higher than those observed for Q4, which involves an unknown confidence in their predictions. The SVM + RDK model predicted only 15 compounds as active against DNMT1, all generated by alvaBuilder, particularly from the ChemDiv DNMT dataset. On the other hand, the FFNN + Morgan model predicted active compounds for all groups of compounds generated by de novo design, with marked differences. For all the groups generated by alvaBuilder, the proportion of compounds predicted as active was higher than 0.13, with DNMT1 actives and UNPD-A being the datasets with higher proportions, while for compounds generated by LigBuilder, this proportion was always below 0.10. This observation could be associated with the differences in fragment acquisition for each software. Namely, alvaBuilder incorporates an internal fragmentation algorithm based on Bemis-Murcko scaffolds, while LigBuilder directly takes fragments from the library.