Core Viewpoint - The rapid development of antibiotic resistance outpaces the discovery of new antibiotics, highlighting the potential of antimicrobial peptides (AMPs) as promising alternatives due to their broad-spectrum antimicrobial activity and unique mechanisms of action [2][6]. Group 1: Antimicrobial Peptides (AMPs) - AMPs are small molecules (10-50 amino acids) that play a crucial role in the host immune defense system, targeting bacteria, fungi, viruses, and parasites [2]. - The mechanisms of AMPs differ from traditional antibiotics, primarily disrupting pathogen cell membranes or interfering with metabolic processes [2][6]. - Despite their potential, the discovery of AMPs remains challenging, necessitating advanced tools like machine learning and deep learning to accelerate research [6][8]. Group 2: Generative Artificial Intelligence in AMP Design - Generative artificial intelligence, particularly through models like AMP-Diffusion, offers a powerful approach for designing AMPs by exploring sequence space systematically [3][7]. - AMP-Diffusion utilizes a pre-trained latent diffusion model to generate potent AMP sequences, ensuring integration with established protein language models like ESM-2 [7][9]. - The model has successfully generated 50,000 candidate AMP sequences, with 76% demonstrating low toxicity and effective bacterial killing capabilities [8][9]. Group 3: Research Findings and Implications - The research team synthesized and validated 46 top-ranking AMP candidates, which exhibited broad-spectrum antimicrobial activity, including against multidrug-resistant strains, with low cytotoxicity [8][9]. - In preclinical mouse models, lead AMPs significantly reduced bacterial load, showing efficacy comparable to polymyxin B and levofloxacin without adverse effects [8][9]. - AMP-Diffusion represents a robust platform for antibiotic design, addressing the urgent need for new antimicrobial agents in the face of rising antibiotic resistance [8][9].