Label encoding. Label encoding assigns a unique in-97 dex ('label') to each sequence element and represents se-98 quences as a (random) list of integers. When combined 99 with deep learning this encoding allows to learn optimal 100 representations during training, starting from randomly ini-101 tialized labels. This differs from one-hot encoding, where 102 the vectors are fixed and pre-defined. Our implementa-103 tion of label encoding supports the post-translational amino 104 acid modifications. We benchmarked peptidy representations using data 107 available in the literature, to predict (i) antimicrobial ac-108 tivity , and (ii) toxicity using machine learning. The five repetitions. Convolutional neural networks (CNNs) 114 were trained on sequence representations, and XGBoost 115 was selected to learn from the peptide descriptor represen-116 tation. These machine learning models were chosen owed 117 to their simplicity and effectiveness . 100 hyperparam- ).