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 selected descriptors (HBA = number of Hydrogen Bond Acceptor, HBD = number of Hydrogen Bond Donor, MW = Molecular Weight, logP = octanol-water partitioning coefficient). For each training set size (1000, 2500, 5000, 7500, 10000, and 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). SMILES enumeration always performed in the top-two approaches across descriptors. When considering the new strategies, atom masking performed overall the best on distribution learning, with functionalgroup masking ranking consistently among the top two approaches for five out of eight descriptors and resulting particularly promising to match the molecular weight and number of hydrogen-bond acceptors and TPSA (better than enumeration). Bioisosteric substitution yielded a relatively poor matching of some properties (e.g., molecular weight, and number of aliphatic rings). On the other hand, it was the best method in matching the number of aromatic rings. Substitution of functional groups can influence certain properties, but not others -which makes bioisosteric replacement useful for specific goals only (e.g., improve selectivity by replacing smaller fragments with bigger ones). Token deletion consistently performed poorly across all properties and sizes for KS values -often even worse than using no augmentation. This is likely due to the detrimental effect of eliminating SMILES tokens on the corresponding molecular properties. Finally, self-training mostly performed as well as or better than not using data augmentation in most cases, with its worst performance for 1000 molecules. This performance trend is expected, since training on smaller datasets (to generate 'augmented' SMILES inputs) challenges the distribution learning capabilities of CLMs (Table , Supp. Table ).