AI‐Based D‐Amino Acid Substitution for Optimizing Antimicrobial Peptides to Treat Multidrug‐Resistant Bacterial Infection

D-amino acid substitution provides an effective strategy for optimizing antimicrobial peptides (AMPs) by enhancing their stability. However, the absence of universal rules renders traditional screening methods time-consuming and labor-intensive, potentially leading to reduced or complete loss of activity. Here, we curated a D-amino acid-substituted AMP dataset from published literature and databases. We then developed ADAPT, an AI-based tool for predicting the functional impact of D-amino acid substitutions, and integrated it into a high-throughput screening pipeline for AMP optimization. Of the variants obtained through this pipeline, 80% exhibited enhanced antibacterial activity. Among these, dR2-1 showed exceptional broad-spectrum antimicrobial activity, reduced toxicity, and substantially improved stability. Mechanistic studies confirmed a membrane-targeting antibacterial mode of action. Furthermore, we engineered a hydrogel delivery system that effectively treated cutaneous infections in mice. Overall, our study established an AI-based framework for D-amino acid substitution in AMPs, enabling the efficient discovery of potent and stable candidates with enhanced clinical translation potential.