The architectures in this research all feature a latent space comparable to that of a VAE, which in theory should constitute the minimal explanation of the data. It is also desirable for the latent space to be ordered in such a way that it is understandable or interpretable for a subject-matter expert. This latent space is used to interpret the model's latent mapping of inputs from the prior distribution. The latent space also allows for visualisation of embeddings and feature clustering of proteins.