The models developed in this research used deep learning to discover embeddings for sequences of amino acids but future work should investigate other peptide representations such as protein structure distance graphs which can embed structural information and SMILES strings used in the world of small drug design. SMILES strings encode chemical information into a sequence of characters thus allowing the models to learn chemical distinctions between the amino acids.