As the chemical space of natural peptides is extremely large, the development of highly accurate classifiers will help accelerate the discovery and development of novel de novo AMPs. In addition, the promising results generated from this study open a number of possible avenues for further work. Our identification of species-specific activity could be improved using a larger external test set, allowing us to draw stronger conclusions. Furthermore, the methodology we have presented is not specific to the classification of AMPs. We suspect that the Kreȋn-SVM coupled with sequence alignment functions could be applied to other biological-sequence classification tasks. Our computational findings demonstrate not only the feasibility of the proposed approach but more generally the utility of the Kreȋn-SVM as a classification algorithm. Its use of indefinite kernel functions provides a means for practitioners to learn from domain-specific similarity functions without the concern of verifying the positive-definite assumption. This is beneficial since it is often well beyond the expertise of the practitioner to verify this assumption. Whilst we have explored its use when combined with sequence alignment functions, there exist many more indefinite kernel functions with which the Kreȋn-SVM could be combined. Furthermore, the theoretical insight of separately regularising the decomposition components of a function in a Kreȋn space could be applied to develop other indefinite kernelbased learning algorithms. A notable example that relates to the current study would be to extend the One-Class SVM to incorporate indefinite kernel functions. The One-Class SVM is a kernelbased learning algorithm that performs anomaly detection . It has previously been applied to identify the domain of applicability of virtual screening models . An indefinite kernel extension of the One-Class SVM could be directly applied to estimate the domain of applicability of our models.