The design of novel and shape-persistent POCs relies on the generative capacity of the model. New POCs are represented as latent variables in the VAE latent space, with their shape per- sistence as the target property to be predicted by the auxiliary predictor directly from their latent variables. In the latent space of the VAE, the latent variables of POCs that are similar to each other are positioned closer within the learnt manifold formed by the continuous representations of POCs. In the structured latent space of the VAE, the resemblances of POCs are manifested in two factors: the similarity of molecular graph features depicted by SMILES and the similarity of the higher-level structure-activity relationship mapped by the auxiliary predictor.