In our previous study, POCs were decomposed into molecular fingerprints of precursors for ML predictions. 9 However, this representation is non-recoverable to the original molecule and therefore it cannot be used in generative modelling. Thus, a new combinatorial representation that integrates both structural features of the precursor components and entire cage molecules needed to be developed. The strategy has recently been proven successful in the molecular design of several reticular frameworks, including metal-organic frameworks (MOFs) and zeolites. MOFs have been decomposed to multiple components, including sequential and one-hot representations, which were fed separately to the VAE-based model with multiple encoder-decoder pairs. In the design of zeolites, each unit lattice was represented by a combination of three three-dimensional representations: the silicon grid, the oxygen grid, and the energy grid, and adopted in a Generative Adversarial Networks (GAN). In this study, we have developed a deep generative model, Cage-VAE, based on the work of Gómez-Bombarelli et al. and Yao et al., but specialised here for the design of POCs. Our model is able to generate novel, valid POCs with the Tri 4 Di 6 topology that are shape-persistent. In addition, a combinatorial encoding system based on POC components, precursors and reactions, was developed to describe the structural and topological features of POCs, showing potential in the efficient representation of large molecular systems. The model architecture is transferable to the generation of other types of cage molecules that exhibit various properties of interest, such as metal-organic cages. The dataset and model are available at .