In the future, we plan to investigate a phenomenon termed selective latent memories due to Kullback-Leibler Divergence constraining. This effect is observed during training and causes a drop in the entropy of certain latent dimensions when the KLD is minimized. In addition, we will generalize from a binary AMP/non-AMP classifier to a multi-class predictor capable of grouping sequences by expected mechanism of action. Finally, as a prelude to the previously mentioned active learning traversal of the space, we plan to investigate the incorporation of structural data into the models, perhaps leveraging the recent success of AlphaFold2 and similar structural prediction algorithms.