17Bacteriocins are ribosomally produced antimicrobial peptides that represent an untapped 18 source of promising antibiotic alternatives. However, inherent challenges in isolation and 19 identification of natural bacteriocins in substantial yield have limited their potential use as 20 viable antimicrobial compounds. In this study, we have developed an overall pipeline for 21 bacteriocin-derived compound design and testing that combines sequence-free prediction of 22 bacteriocins using a machine-learning algorithm and a simple biophysical trait filter to generate 23 minimal 20 amino acid peptide candidates that can be readily synthesized and evaluated for 24 activity. We generated 28,895 total 20-mer peptides and scored them for charge, a-helicity, and 25 hydrophobic moment, allowing us to identify putative peptide sequences with the highest 26 potential for interaction and activity against bacterial membranes. Of those, we selected sixteen 27 sequences for synthesis and further study, and evaluated their antimicrobial, cytotoxicity, and 28 hemolytic activities. We show that bacteriocin-based peptides with the overall highest scores for 29 our biophysical parameters exhibited significant antimicrobial activity against E. coli and P. 30 aeruginosa. Our combined method incorporates machine learning and biophysical-based 31 minimal region determination, to create an original approach to rapidly discover novel 32 bacteriocin candidates amenable to rapid synthesis and evaluation for therapeutic use.