In order to inspect the learnt latent space, Principle Component Analysis (PCA) was used to visualise the position of POCs marked with properties in the compressed space as shown in Figure (a). Figure (a), 5(b) and 5(c) show the PCA analysis performed on the latent space of Cage-VAE jointly trained with the predictor, while Figure (d) shows the VAE trained without the predictor. In Figure (a), the latent vectors of both the original and augmented datasets are used for PCA analysis and the probability of shape persistence prediction was used to mark data points in the PCA reduced dimension. A gradient of the probability of shape persistence mapped by the predictor is seen, clearly illustrating a smooth and continuous transition spectrum from the collapsed (light yellow) region to the non-collapsed region (dark violet). As discrete variables are disadvantageous in creating a gradient in the compressed latent space, we used a continuous representation of our discrete variable. Here, shape persistence is reflected as a probability that the POC will be shapepersistent. In both Figure (b) and 5(c), only latent vectors of the original dataset were used in the PCA analysis. However, the probabilities of shape persistence predictions from the predictor and ground-truth labels were used in Figure (b) and 5(c), respectively. The similar pattern exhibited in these two latent spaces demonstrates that the predictor is accurate in mapping the cage latent vectors to their shape persistence feature and that the latent space is well organised. In Figure Next, interpolation was used to explore the latent space. When traversing from the initial to the final data points, novel POCs can be created across the trajectory of interpolation, three benzene rings. Another structural feature is that the length and complexity of BB2 backbones gradually increase from the initial to the final molecules. It is interesting that although only ordinal encodings were employed, BB1 skeletons also exhibit a structural transition from simpler to complex structures. In fact, BB1 skeletons and reactions are both observed to have transitions at a larger scale in the latent space. The approach to visualise the larger-scale transition can be simply to interpolate between POCs at a larger distance or interpolate across known transition boundaries.