In this work, we utilised the Kreȋn-SVM algorithm to assess the effectiveness of sequence alignment functions for AMP classification. We performed an empirical comparison of both the local alignment score and Levenshtein distance on two AMP datasets from the literature. Furthermore, we tested the ability of our approach to detect species-specific activity on a dataset of experimentally validated peptides measured against both Staphylococcus aureus ATCC 29213 and Pseudomonas aeruginosa ATCC 27853, henceforth referred to as S. aureus ATCC 29213 and P. aeruginosa ATCC 27853. Our trained models are made available as web applications at , for the prediction of both general and species-specific activities.