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Antimicrobial peptides (AMPs) are promising candidates to address the global antimicrobial resistance crisis, yet their traditional design remains labor-intensive and inefficient. Here, we developed BroadAMP-GPT, an integrated computational-experimental framework that combines AI-driven generation, multi-tiered screening, and experimental validation to rapidly discover potent AMPs with broad-spectrum activity. Using this platform, 57% of AI-generated candidates exhibited potent efficacy against ESKAPE pathogens – key culprits of multidrug-resistant infections. An outstanding candidate, AMP_S13, demonstrated exceptional stability under diverse physiological conditions, including extreme pH (2–10), proteolytic exposure, and elevated temperatures, while maintaining minimal cytotoxicity and low hemolytic activity. AMP_S13 also showed robust in vivo efficacy, reducing mortality in Galleria mellonella infection model and accelerating wound healing in a murine MRSA skin infection model. These results validate BroadAMP-GPT as a transformative tool for accelerating the discovery of stable, broad-spectrum and low-toxicity AMPs, offering a scalable strategy to address the urgent threat of multidrug-resistant pathogens.
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Li et al. (2025) studied this question.
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