Next, we evaluated each augmentation method for its ability to match the physico-chemical properties of the training set ('distribution learning'). To this end, we computed eight properties: number of aliphatic and aromatic rings, molecular weight (MW), octanol-water partition coefficient (logP), number of hydrogen bond donors (HBD) and acceptors (HBA), topological polar surface area (TPSA), and number of rotatable bonds. The similarity between the training set and the de novo designs was measured via the Kolmogorov-Smirnov (KS) distance 34 (the lower, the higher the similarity).