In contrast to the previous section, where we focused on PC pairs, in this section, for ease of calculation and visualization, we employ only a single variable per model variant. We choose the principal component (PC) having the largest Silhouette Score with verified-AMP/non-verified-AMP labeling (see Fig. and further discussion of PC identification in Sec 3.4). We identify the "width" of the latent space by calculating the mean and standard deviation over the embedded sequences. Then we sample from ten evenly spaced regions that form a "line across" the latent space, limiting the min and max values of the region centers to ±4 times the standard deviation. The random sampling performed in each region follows a normal distribution with the mean determined by the region center and the standard deviation set to 20% of the standard deviation of the latent space. For external validation on a large number of generated sequences-difficult to perform experimentally-we assess in each neighborhood the probability of generating AMPs ρ AMP (Fig. ) by employing a previously published QSAR model. AMPlify is a deep neural network AMP classifier that has been shown to achieve 93.71% accuracy on the task of AMP classification .