OmegAMP: Targeted AMP Discovery via Biologically Informed Generation

Antimicrobial peptide (AMP) discovery is often limited by poor controllability and low experimental hit rates. We introduce OmegAMP, a diffusion-based framework for reliable AMP generation with fine-grained control over physicochemical properties and activity profiles. OmegAMP leverages a biologically informed encoding space and a novel synthetic data augmentation strategy for classifier-based filtering, which significantly reduces false positive rates. In silico experiments demonstrate state-of-the-art performance across the discovery pipeline. Crucially, in wet-lab validation, 24 out of 25 (96\%) OmegAMP-designed peptides demonstrated antimicrobial activity, including effectiveness against multi-drug resistant strains. Our results highlight OmegAMP's potential to accelerate the development of novel therapeutics against antimicrobial resistance.