abstract: Antimicrobial resistance threatens to become a leading cause of death by 2050, and antimicrobial peptides (AMPs) offer a promising alternative because bacteria acquire resistance to them more slowly than to conventional antibiotics. Deep generative models design AMP candidates at scale, but translating this into clinical leads requires three capabilities: controllable generation toward specified properties, evaluation of computational predictors against biological activity rather than database membership, and extensive wet-lab validation. This talk presents three contributions addressing these capabilities: HydrAMP, BATTLE-AMP, and OmegAMP. HydrAMP is a conditional variational autoencoder that disentangles peptide sequence from antimicrobial conditions and supports unconstrained and analog generation with parameter-controlled creativity. In experiments across five bacterial strains, nine analogs of clinically relevant active prototypes and six analogs of an inactive prototype showed high antimicrobial activity. BATTLE-AMP benchmarks AMP predictors against experimentally measured minimum inhibitory concentrations (MICs) rather than database membership. We surveyed 48 methods, found fewer than 25% reproducible, and evaluated 21 model variants. MIC-trained models consistently outperform binary classifiers; most rely on amino acid composition rather than residue order; and activity cliffs remain unresolved by both machine learning and molecular dynamics. OmegAMP extends controllable generation through a diffusion model with biologically informed encoding, combining three generation modes (de novo, analog, motif-guided) with physicochemical and species-specific conditioning. In experiments across 20 bacterial strains including multidrug-resistant clinical isolates, 86% of 95 de novo peptides achieved MIC $\leq 2 \mu M$, 93% of analogs converted inactive prototypes into active antimicrobials, and motif-guided generation introduced antimicrobial activity while preserving LPS- or DNA-binding function.