We started with three publicly available models modified from work by , in which the authors compared a recurrent neural network (RNN), a RNN with an attention layer and a Transformer to assess their respective capabilities for generating SMILES strings describing novel molecular compounds for drug design. The models all made use of a VAE architecture that generated smooth latent spaces. The results demonstrated the benefits of including self-attention to deep generative models, and the authors concluded there is a trade-off between variational models in terms of their ability to properly reconstruct input data and their ability to embed complex information about the inputs into a continuous latent space. We modified these three models to take as input and output sequences of up to fifty amino acids rather than SMILES strings. In addition, we derived two more models, an adversarial autoencoder (AAE) , and a Wasserstein autoencoder (WAE) , by modifying the latent spaces and architectures of the original three.