The results, depicted in Figure for antimicrobial activity prediction and Figure for hemolysis prediction, confirmed our earlier observations (performances in Table ). Notably, the RNN performances were higher than those observed in the screening experiment, and were clearly above those of GTP-3. hemolysis. The finding that simpler machine learning architectures, like SVM, can rival the performance of more complex RNNs in predicting antimicrobial activity and hemolysis is particularly interesting. A comparison with models trained on similar datasets, which achieve similar performances as reported in this study, further reinforces the consistency of our findings. This raises questions about the importance of model architecture versus foundational elements such as data quality and feature engineering. It suggests that a balanced approach, prioritizing optimization of these foundational components, could prove more beneficial than focusing solely on model complexity. we were curious to see whether a spatial separation of actives/inactives and hemolytic/nonhemolytic sequences can be obtained from encoding with MAP4C, the chiral version of MAP4, possibly explaining the good performance of the MAP4C SVM model. For this, we reduced the 2048-dimensional feature space of MAP4C to 2D using the dimensionality reduction method TMAP, and used the obtained visualization to display a set of molecular properties.