Rebuilding the Antibiotic Pipeline with Guided Generative Models

The antibiotic pipeline has stalled: most recent approvals reflect incremental modifications of existing scaffolds, while antimicrobial resistance continues to outpace discovery. Antimicrobial peptides (AMPs) offer a compelling alternative because of rapid, multi-modal activity, but clinical translation has been limited by toxicity, serum instability, and the prohibitive cost of synthesizing and testing large libraries. Recent progress in protein language models (pLMs) changes the computational landscape by providing embeddings that capture sequence context and biophysical regularities from massive unlabeled datasets. However, pLMs alone are not a design solution. We propose a technique coupling pLM-derived representations to diffusion or discrete flow-based generative models that can explore non-homologous regions of peptide space while being steered by multi-objective guidance. This framework supports direct optimization for potency, selectivity, and developability during generation, compressing hit discovery and early optimization into a single in silico loop. Conditioning generation on target and safety predictors could shift AMPs from membrane-lytic ‘blunt instruments’ toward more selective, target-aware therapeutics.