Table reports the results of all models on the predefined test set associated with each dataset. For the sake of completeness, we also include the performance of the neural network-based classifiers proposed by the authors of each dataset. As for the nested cross-validation results, we observe that all models perform better on the AMPScan dataset than the DeepAMP dataset. Considering the former, the local alignment score achieves the largest accuracy and AUC, and is followed closely by the literature model. On the DeepAMP dataset, the performance is similar among all methods. The local alignment score achieves the best AUC but the Levenshtein distance achieves the best accuracy. However, the Levenshtein distance outperforms the literature model both in terms of accuracy and AUC. On both datasets, the baseline is the least predictive model. It is encouraging to observe that the sequence alignment functions can produce classifiers that match, and also outperform, the neural network-based classifiers.