We then proceed to choose variables of interest to hold constant in the latent space and perturb the others. This will allow us to find new peptides with the properties of interest maintained. We choose to maintain the verified-AMP-correlated PC and the hydrophobicity-correlated PC, to produce the sequences likelier to share experimental AMP-likeness and hydrophobicity-an important AMP property-with GL13K. We note that this procedure could be done with any identified property near any input point of interest. We use the previously mentioned correlation coefficients (Sect. 3.4), to find the verified-AMP correlating PC and the top hydrophobicity correlating PC. Having kept track of the PCA vector for our AMP of interest, we perform sampling by keeping our two PC's of interest, AMP and hydrophobicity correlated, fixed and adding gaussian noise to all other PC's. The added noise variance must be tuned individually for each model as the latent space organization is different for each model. The variance tuning is performed by identifying the mean and standard deviation of all training dataset points in PCA space and then sampling Gaussian noise centered at the mean with 1/5 the standard deviation. This noise vector is then summed with the PCA vector of our AMP of interest, thus shifting the sampling location to be near the AMP of interest. Once we have the noisy nearby PCA samples we perform an inverse PCA transformation returning to our latent space vector representation, demonstrating one particular valuable property of the PCA approach. We then pass the noisy latent samples to the decoder which will generate the desired candidate peptide sequences.