To assess the usage of learning with sequence alignment functions, we performed a series of computational experiments on a number of AMP datasets. In each of our evaluations, we tested both the local alignment score (LA) and the Levenshtein distance (LEV) in conjunction with the Kreȋn-SVM algorithm. We compared against a baseline SVM using the gapped k-mer kernel. This is a positive-definite kernel function that has produced accurate models in a number of biological-sequence classification tasks and hence makes for a useful baseline. When applicable, we also compared our models with AMP identification tools from the literature. The parasail package was used to compute local alignment scores. We only considered normalised variants of the local alignment score and Levenshtein distance, with the normalisation performed according to Schölkopf et al. and Yujian and Bo , respectively. We report the accuracy and the area under the receiver operating characteristic curves (AUC) to compare models. The accuracy is defined as the proportion of correctly classified instances and the AUC as the probability that a classifier will score a randomly chosen positive instance higher than a randomly chosen negative instance . In order to allow for a fair comparison, all models used the same training and test splits. The optimal hyperparameters were selected by performing an exhaustive grid search over the training set, using 10-fold cross validation. The λ hyperparameter of the SVM algorithm, as well as the λ + and λ -hyperparameters of the Kreȋn-SVM algorithm, were selected from {0.01, 0.1, 1, 10, 100}. The Levenshtein distance has no hyperparameters to control and we used the default values for the hyperparameters of the local alignment score. The gapped k-mer kernel has two hyperparameters g and m and is quite susceptible to their values. The optimal value of g was selected from {1, 2, 3, 4, 5} and the optimal value of m was selected from {1, 2, 3, . . . , 10}. It is required that g > m, so only valid combinations of the two were considered. In our nested crossvalidation experiments, we used 10 inner and 10 outer folds and the reported results are averaged over the outer fold test sets.