Supporting Table . Distribution learning of physico-chemical properties. We report the Kolmogorov-Smirnov (KS) distance between the de novo designs (4000 SMILES strings) and the training set molecules, computed for four rest descriptors (number of aliphatic or aromatic rings, rotatable bonds, and TPSA= topological surface). For each training set size (1000, 2500, 5000, 7500, 10000, 25000 molecules), the KS distance is reported for each augmentation strategy, and each descriptor. For each descriptor and training set size, the best and second-best KS distances are highlighted in bold and underlined, respectively. The number of times a given augmentation strategy provides the best or second-best performance for a given descriptor across training set sizes is also reported. The KS distances between the training and the test set molecules, and for the designs obtained with no augmentation are reported as a reference (n.a. = not available).