There are multiple types of generative models; the two most well-known and often-used are VAEs and Generative Adversarial Networks (GANs). We choose to employ VAE-based models because VAEs boast a number of properties of particular relevance for our specific case of developing smooth, interpretable latent spaces with potential use as search spaces for computer-aided biomolecular design. In comparison to GANs, VAEs have a more natural formulation of an associated latent space, with a rigorous mathematical derivation from Bayesian probability theoryKingma and Welling . The assumption of a Gaussian prior enforces a certain level of smoothness and continuity that is desirable in a design space, and allows for the sampling of Gaussian distributions "near" any defined point in the space. Finally, on a practical note, they have demonstrated utility in the field of de novo small molecule designGómez-Bombarelli et al. , and we wanted to determine their utility for sequence-based AMP design as well. VAE's also feature an encoder which can directly map new samples of interest to their respective latent vectors, a feature not present, out of the box, in GAN architectures.