A variational autoencoder (VAE) is an architecture to address the generative design problem with a high degree of flexibility in model construction and architecture, resulting in a highly modular approach. VAEs are capable of transforming molecules into continuous and compact representations in a latent space, where the patterns and structures in the collection of molecules can be captured and therefore generate new samples. VAEs have been applied to molecule generation and shown adaptability with multiple molecular representations ranging from one-to three-dimensions. In molecular science, Gómez-Bombarelli et al. first used a VAE with an external predictor to model small molecules and transfer them to continuous representations in the latent space, enabling the conditional exploration of molecules in the latent space via optimisations. Yao et al. then developed a VAE architecture capable of realising the conditional design of MOFs. However, a VAE for POCs has not yet been introduced.