Next-Generation Antimicrobial Peptides for Biofilm-Associated Infections: Engineering, Biomaterial Delivery and AI-Assisted Discovery

Abstract

Antimicrobial peptides (AMPs) are increasingly regarded as next-generation antimicrobial agents because of their broad-spectrum activity, rapid killing, antibiofilm potential, immunomodulatory properties, and mechanisms of action that differ from those of many conventional antibiotics. Despite these advantages, their clinical translation remains limited by proteolytic instability, hemolysis or cytotoxicity, poor pharmacokinetics, salt and serum sensitivity, production costs, and delivery challenges. The AMP field is therefore shifting from natural peptide discovery toward integrated engineering pipelines that combine rational peptide modification, biomaterial-based delivery, high-throughput screening, and artificial intelligence (AI), particularly machine learning (ML) and deep learning approaches. Chemical and structural modifications, including D-amino acid substitution, N-glycine substitution, cyclization, lipidation, PEGylation, terminal amidation, hydrocarbon stapling, hybridization, sequence truncation, metal coordination, and biomaterial immobilization, are being used to improve stability, potency, selectivity, antibiofilm activity, and tissue localization. In parallel, AI-guided approaches, including ML, deep learning, and generative modeling, enable large-scale exploration of diverse peptide sources, including microbiomes and extinct proteomes, to entirely new sequences, while supporting optimization of potency, selectivity, stability, toxicity, and synthesizability. This focused review summarizes recent advances in AMP engineering, biomaterial-assisted delivery, and AI-guided discovery for biofilm-associated infections in the context of antimicrobial resistance.