PepGen-FB: Multi-functional antimicrobial peptide generation via curriculum learning-guided multi-objective feedback optimization

Antimicrobial peptides (AMPs) emerge as a type of promising therapeutic compounds that exhibit broad spectrum antimicrobial activity with high specificity and good tolerability. However, current AI-based AMP design strategies, which primarily rely on learning the distribution of natural AMPs, fail to overcome the inherent trade-off between antimicrobial activity and toxicity, thereby hindering their clinical translation. In this work, we propose PepGen-FB, a novel multi-objective optimization method for optimizing desired properties for AMPs iteratively. It employs a curriculum learning-guided feedback mechanism to iteratively guide the generative model to smoothly optimize AMPs with improving antibacterial activity and decreasing toxicity. Comprehensive experiments demonstrate that the AMPs designed by PepGen-FB substantially outperform natural prototypes in achieving an optimal balance between high antimicrobial activity and low cytotoxicity, improved the generation success rate from 7.1% to 96.2% compared to the ProGen2 model. Further motif analyses provide interpretative support for the optimization process. PepGen-FB enables the seamless integration of arbitrary black-box predictors while ensuring optimization stability through curriculum-guided feedback, which establishes a novel data-driven optimization paradigm.