For both, prediction of antimicrobial activity and prediction of hemolysis, the topperforming models were the MAP4C SVM and the RNN model trained on sequence data, the latter being the best performer in our original work (Table ). The performances for both models were in a similar range, although the RNN displayed a notably higher ROC-AUC in both tasks. GPT-3. predictions. On the other hand, the features SVM trained only on helicity and hydrophobic moment did not perform significantly above background, and was later used as a negative control model.