In the past five years, there has been an explosion of research in the field of deep generative models for small molecule drug design Segler et al. . Most work in this area has, until now, has been focused on the design of small molecules, with only a few studies on the design of short peptides Van Oort et al. , but in the last year or two research in this area has begun to grow . For example, constructed a Wasserstein Autoencoder (WAE) model that formed a complete pipeline for de-novo protein design and identified two sequences with potent antimicrobial activity against E.coli. In another paper by the same teamSercu et al. , they created a tool, (IVELS), for analysing VAE models, throughout the training process, with which they could select models that perform best. Two other relevant recent studies have reported on the use of an Long Short-Term Memory (LSTM) model to design peptides with experimentally-verified activity against multi-drug resistant pathogens, and the use of generative Recurrent Neural Networks (RNNs) to design non-hemolytic AMPs .