To generate a visual representation of the chemical space of the de novo designed libraries and reference data sets, we used t-distributed stochastic neighbor embedding (t-SNE) and principal component analysis (PCA) based on structural fingerprints and continuous properties of pharmaceutical interest. The structural fingerprints used in this study were Extended Connectivity Fingerprints radius two (ECFP4) and MACCS Keys (166-bits), which were computed for all molecules using the RDKit library . Six molecular properties relevant to pharmaceuticals were computed: MW, HBA, HBD, TPSA, logP, and number of rotatable bonds (nRotB).