The model was trained using a combination of reconstruction loss, Kullback–Leibler (KL) divergence, and classification loss, optimized for balanced performance. For peptide generation, latent vectors ...

If too many hidden nodes are used, the autoencoder will essentially memorize the source data -- overfitting the data, and the model won't generalize when previously unseen data is encountered. As a ...