A novel generative framework for designing pathogen-targeted antimicrobial peptides with programmable physicochemical properties.

ABSTRACT: Antimicrobial peptides (AMPs) are crucial in addressing the global crisis of bacterial resistance. However, there are still significant limitations in existing methods on de novo AMPs design, especially in designing AMPs with desirable physicochemical properties for specific bacterial pathogens. In this study, we propose a novel generative framework for designing pathogen-targeted antimicrobial peptides with programmable physicochemical properties. More specifically, a conditional Variational Autoencoder is first pretrained for generating AMPs with editable physicochemical properties. We then develop a conditional diffusion model to learn hidden representations of AMPs for targeting pathogens of interest, and construct corresponding MIC predictors for specific bacterial strains. Through co

SUBMITTER: Zhao W 

PROVIDER: S-EPMC12747415 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature