Harnessing generative AI for predicting and optimizing antimicrobial peptides against drug-resistant infections.

Antimicrobial resistance (AMR) poses a major global health threat that demands the discovery of new antimicrobial agents. Antimicrobial peptides (AMPs) offer a promising therapeutic alternative due to their broad-spectrum activity and reduced likelihood of resistance development. In the current study, we developed COMPASS, a comprehensive database aggregating 75,381 unique AMP sequences from nine public repositories, and created AmpGPT2, a transformer-based generative model specifically fine-tuned for AMP sequence generation. Unlike directed approaches, which optimize antimicrobial sequences or certain properties, our foundational model learns general AMP sequence patterns through an undirected training strategy. AmpGPT2 generated peptide sequences, of which 95.41% were predicted to be AMPs by AMP Scanner, representing a substantial improvement over existing models. The generated peptides exhibit physicochemical properties consistent with natural AMPs, including appropriate length distributions and molecular characteristics. Experimental validation demonstrated that one of five tested peptides, which shares structural features with dermaseptin-family AMPs, exhibited significant concentration-dependent antimicrobial activity against Klebsiella pneumoniae and Pseudomonas aeruginosa, supporting the model's potential for functional AMP discovery. Highlighting the persistent challenge of translating computational predictions into biological function, this work establishes a foundational framework for AMP discovery that can serve as a basis for subsequent directed optimization strategies, potentially accelerating the development of novel antimicrobial therapeutics.