Several studies have highlighted the promise of ML algorithms in predicting the antimicrobial activity, dissecting the complex SAR, and informing the drug design of AMPs . A wide range of ML algorithms have been utilised, including random forests , support vector machines (SVMs) and artificial neural networks . Many of these algorithms are used in combination with a carefully selected set of peptide features, which can be divided into two categories: compositional and physicochemical. The amino acid composition is the simplest example of a compositional feature, which is a vector containing counts of each amino acid in a given peptide. There are various extensions, such as the reduced amino acid composition and the pseudo amino acid composition . When computing the reduced amino acid composition, a peptide is represented in a reduced alphabet in which similar amino acids are grouped together. The pseudo amino acid composition accounts for composition as well as sequence-order information, as this is not considered in the standard amino acid composition. The set of physicochemical features include peptide properties such as the charge, hydrophobicity and isoelectric point . These features are typically average values of the respective properties calculated over the length of the peptide.