Training deep generative models requires large datasets (normally larger than 10 6 ); the size of the original dataset in this work (35,802 POCs) was therefore insufficient. The size limitation originates from the highly limited instances of BB1 and BB2 in the original dataset. Due to the disassembled representation of POCs, one or several components can be augmented. Here, as BB2s are modelled using SMILES representations, it was naturally chosen to be the target of data augmentation. Therefore, a two-step combinatorial data augmentation strategy was used to boost the number of POCs. There is a potential benefit in choosing BB2 to be represented by SMILES from the perspective of data augmentation.