Generative latent diffusion language modeling yieldsanti-infective synthetic peptides by Marcelo D.T. Torres & Leo Tianlai Chen & Fangping Wan & Pranam Chatterjee & Cesar de la Fuente-Nunez instant download

Generative latent diffusion language modeling yieldsanti-infective synthetic peptides by Marcelo D.T. Torres & Leo Tianlai Chen & Fangping Wan & Pranam Chatterjee & Cesar de la Fuente-Nunez instant download

THE BIGGER PICTURE Antibiotic resistance is accelerating faster than our ability to discover new drugs.Antimicrobial peptides (AMPs) are promising alternatives, but navigating the immense sequence space whilemeeting potency and safety constraints has been a bottleneck. Here, we introduce AMP-Diffusion, a generative artificial intelligence (AI) platform that uses latent diffusion modeling and protein language embeddingsto create biologically relevant results without predefined motifs or structural priors. From 50,000 in silico designs, we synthesized and tested 46 peptides: 76% inhibited bacteria, including multidrug-resistant strains,while showing minimal toxicity. Two lead candidates showed in vivo efficacy in mice that was comparable toclinically used antibiotics. This study illustrates how generative AI can rapidly identify and optimize therapeutic peptides, offering a scalable and generalizable approach to antibiotic development. AMP-Diffusion setsthe stage for future platforms that tailor peptides for specific pathogens or therapeutic targets.