AI‐Guided Design of Antimicrobial Peptide Hydrogels for Precise Treatment of Drug‐resistant Bacterial Infections

Traditional biomaterial development lacks systematicity and predictability, posing significant challenges in addressing the intricate engineering issues related to infections with drug-resistant bacteria. The unprecedented ability of artificial intelligence (AI) to manage complex systems offers a novel paradigm for materials development. However, no AI model currently guides the development of antibacterial biomaterials based on an in-depth understanding of the interplay between biomaterials and bacteria. In this study, an AI-guided design platform (AMP-hydrogel-Designer) is developed to generate antibacterial biomaterials. This platform utilizes generative design and multi-objective constrained optimization to generate a novel thiol-containing high-efficiency antimicrobial peptide (AMP), that is functionally coupled with hydrogel to form a complex network structure. Additionally, Cu-modified barium titanate (Cu-BTO) is incorporated to facilitate further complex cross-linking via Cu²⁺/SH coordination to produce an AI-AMP-hydrogel. In vitro, the AI-AMP-hydrogel exhibits > 99.99% bactericidal efficacy against Methicillin-resistant Staphylococcus aureus (MRSA) and Escherichia coli (E. coli). Furthermore, Cu-BTO converts mechanical stimulation into electrical signals, thereby promoting the expression of growth factors and angiogenesis. In a rat model with dynamic wounds, the AI-AMP hydrogel significantly reduces the MRSA load and markedly accelerates wound healing. Therefore, the AI-guided biomaterial development strategy offers an innovative solution to precisely treat drug-resistant bacterial infections.