As reference models, we used our previously reported Naïve Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), and Recurrent Neural Network (RNN) classifiers trained on the same data. We furthermore trained two additional SVM models on alternative representations of peptide sequences: one utilizing the MAP4C fingerprint with a custom Jaccard kernel, and another using predicted fraction of helical residues and hydrophobic moment with a linear kernel. Fraction of helical residues were predicted using SPIDER3. Hydrophobic moment was computed using the method of Eisenberg et al. To explore the potential of GPT-3 models for antimicrobial and hemolytic activity classification, we performed fine-tuning of the Ada, Babbage, and Curie models which were accessible through the OpenAI API (v0.28.0, accessed between 25.05.2023 and 01.06.2023). The fine-tuning process involved training each model using the full, 20% and 2% sets for activity classification and the full and 10% set for the hemolysis classification. In the later evaluation with the more advanced LLM GPT-3.5 Turbo, fine-tuning was also performed via OpenAI's Python API (v1.11.1), following the provided guidelines, but we restricted ourselves to the full model. The utilized fine-tuning datasets contained a system role ("predicting antimicrobial activity/hemolysis from an amino acid sequence"), a user message (peptide sequence formatted as "SEQUENCE ->"), and a system message ("0" for negative labels and "1" for positive labels).