UCSF

Antimicrobial resistance poses an urgent threat to global health. The development of new antimicrobials is crucial. Synthetic copolymers are attractive as a potential tool because they can be produced at scale and designed to mimic antimicrobial peptides and act as broad-spectrum antimicrobials capable of evading resistance mechanisms. This work leverages a cross-molecular machine learning pipeline, trained on antimicrobial peptides, to develop potent antimicrobial polymers—which were then synthesized and validated experimentally—to combat Escherichia coli. Crucially, all polymers predicted by the active learning pipeline during exploitation sampling exhibited impressive antimicrobial activity. One candidate copolymer was further characterized and shown to permeabilize the bacterial membrane, which is associated with decreased resistance. Furthermore, this copolymer demonstrated remarkable synergy in eradicating biofilm-associated E. coli when combined with a clinical drug regimen. These results demonstrate promise for potentiating antibacterial activity of currently available antibiotics, treating serious and complicated infections, and combatting antimicrobial resistance.