We follow the methodology of Vishnepolsky et al. to construct useful training datasets for our problem. We utilised the Database Three seperate datasets of peptides with activity measured against S. aureus ATCC 29213, S. aureus ATCC 25923 and P. aeruginosa ATCC 27853 were created using the data collected from DBAASP. We refer to these datasets as SA 29213 , SA 25923 , and PA 27853 , respectively. Each dataset is constructed from highly active peptides (MIC ≤ 25µg/mL) and inactive peptides (MIC ≥ 100µg/mL). A peptide with 25µg/mL < MIC < 100µg/mL would not be included in our training dataset. This large interval allows us to account for experimental errors, which in turn increases the confidence in our class labels. In the case that a peptide was associated to multiple activity measurements, the median value was taken to represent its activity. As shown in table 1, the three training datasets are all relatively small and contain slightly more active peptides than inactive peptides.