In this section, we highlight the ability of our models to identify AMPs that are active against specific species, particularly S. aureus ATCC 29213 and P. aeruginosa ATCC 27853. Table displays the accuracy on the external test set for models trained on the AMP-Scan and DeepAMP datasets. Once again, we also include the performance of the neural-network-based classifiers proposed by the authors of the AMPScan and DeepAMP datasets. We observe that the performance of all models is very poor. We noticed in our investigations that the majority of models predicted active for a large proportion of the peptides. This general poor performance is to be expected. We attribute it to the fact that these models have been trained to recognise if a peptide exhibits antimicrobial activity against any type of species. It is therefore unreasonable to assume that they are able to discriminate activity against specific species. Table displays the accuracy on the external test set for models trained on the species-specific datasets. We also present the performance of the web-tools provided by Vishnepolsky et al. As mentioned in section 2.3.3, at the time of publication, there is no web-tool to predict activity against S. aureus ATCC 29213. Hence, we also provide results for models trained on SA 25923 and tasked with predicting activity against S. aureus ATCC 29213. There is clearly a general improvement over the models trained on the AMPScan and DeepAMP datasets, indicating that the models have much greater discriminative power. On the SA 29213 dataset, all models achieve the same accuracy. The baseline achieves the greatest AUC, but is closely followed by the Levenshtein distance. Considering the SA 25923 dataset, the Levenshtein distance produces a model with the same accuracy as the web-tool but with a larger AUC. It is interesting to note that the models trained on SA 25923 can still make accurate predictions on S. aureus ATCC 29213. Whilst these are two different strains, the findings suggest that the antimicrobial susceptibility to the AMPs is similar for both strains, implying similar mechanisms work in the same species. Considering the PA 27853 dataset, we find that the local alignment score, Levenshtein distance and baseline produce equally accurate models, all of which are more accurate than the web-tool. However, the AUC of the local alignment score and Levenshtein distance are considerably higher than that of the baseline. Whilst it is difficult to make any strong conclusions on such a small dataset, it is still encouraging to observe that our models achieve similar, and sometimes greater, accuracy than both the baseline models and web-tools.