Starting from the DBAASP dataset of 9,548 peptide sequences annotated with antibacterial activity and 2,262 peptide sequences annotated with hemolysis effect, we had previously evaluated NB, RF, SVM and RNN models, and found the latter to perform best for predicting both activity and hemolysis from sequence data. For additional reference, we trained an SVM on the fraction of helical residues and the hydrophobic moment, two properties commonly known to correlate with antimicrobial activity, as well as another SVM on MAP4C, a molecular fingerprint that can reliably encode large molecules such as natural products and peptides including their chirality, a parameter which we considered important since our data listed sequences containing both L-and D-amino acids.