Two metrics were obtained from the CSR curves: area under the curve (AUC) and the fraction of scaffolds to retrieve 50% of the compounds in the database (F50) (i.e., if a dataset has F50=0.43, 50% of the compounds in the dataset are distributed in 43% of the scaffolds). A data set with maximum diversity would contain a different scaffold for each molecule in the library and the curve would be a diagonal with AUC of 0.5. As the scaffold diversity decreases the curve will move away from the diagonal. The minimum diversity would be a data set where all the compounds have the same scaffold. In this case, the CSR function would be a vertical line with AUC equal to 1.0. The fingerprint-based diversity was assessed with the mean of the paired Tanimoto similarity (MPTS), using the MACCS keys (166-bit) fingerprint. In the consensus diversity plots (Figures and) it is shown that FDA-approved small-molecule drugs is the dataset with the highest scaffold and fingerprint-based diversity (AUC=0.80, F50=0.028 and MPTS=0.29), followed by LANaPDB (AUC=0.87, F50=0.007 and MPTS=0.47) and COCONUT (AUC=0.90, F50=0.002 and MPTS=0.39). This result can be attributed to the fact that this dataset has not just NPs, instead a significant proportion are NP-derivatives and purely synthetic molecules, which increases the structural diversity. According to the MPTS metric, the side chain structural diversity of LANaPDB is lower than COCONUT. Nonetheless, considering the AUC and F50 metrics, LANaPDB has higher scaffold diversity than COCONUT, nevertheless, the difference between both databases considering these two metrics is small (ΔAUC=0.03 and ΔF50=0.005). Therefore, the structural diversity of LANaPDB is very similar to COCONUT, with less side chain diversity and a little more scaffold diversity.