To test the ability of our methodology to identify species-specific activity, we have utilised an external dataset of 16 peptides with minimum inhibitory concentration (MIC) measured against S. aureus ATCC 29213 and P. aeruginosa ATCC 27853. To make this dataset suitable for classification, we label a peptide as active if its MIC < 100µg/mL and inactive otherwise. Further details of the microbiological experimentation used to construct this dataset can be found in section 2.4. Vishnepolsky et al. have shown that, given an appropriate training dataset, predictive models of peptide activity against specific species can be constructed. This involves training a separate model for each species of interest. Their model predicting activity against E. coli ATCC 25922 achieved a balanced accuracy of 0.79, which was greater than a number of common AMP prediction tools . Furthermore, models to predict activity against S. aureus ATCC 25923 and P. aeruginosa ATCC 27853 were made publicly available as web-tools.