All models feature an associated property predictor in the form of a binary classifier that predicts whether the latent representation of a given peptide is an verified-AMP or not. This classifier is a two layer fully connected neural network trained with a binary cross-entropy loss that is joined to the KL-divergence loss and the peptide reconstruction accuracy loss of each model.