UNPD-A is also the most diverse according to the ECFP4 fingerprint, followed closely by the ChEMBL's DNMT1 inhibitors and molecules designed from FooDB (Table ). ECFP4 similarity is considerably lower and has very close values among the nine databases, evidenced by the proximity of the curves (Figure ). This is expected because ECFP4 codifies connectivity features along the selected radius, while MACCS Keys has a pre-defined list of features, giving the first more resolution . In the case of molecular docking scores, the nine alvaBuilder databases have median values around the reference database for LeDock, as well as for Vina (Figure ). Compounds designed from FooDB and UNPD-A present less auspicious medians; this could be related to the molecular size. We have observed that ligand efficiency (LE), computed as the docking scores divided by the number of heavy atoms, improved the correlation between docking scores and biological activity. For that, LE values were calculated, and the results can be found in Tables and. While means of FooDB and UNPD-A tend to the value of ChEMBL inhibitors with Vina LE, LeDock LE keeps the same trend as the scores.