Antimicrobial resistance causes over a million deaths annually. Antimicrobial peptides (AMPs) are a promising solution, yet existing generative AMP models are not ready to design peptides with non-natural amino acids and/or chemical modifications, which are essential for real-world peptide drugs. We present AMPGAN v3, a multi-objective conditional GAN that expands the generative vocabulary to D-amino acids and N/C-terminus modifications such as amidation.
By separating adversarial and activity-aware supervision across two specialized discriminators, AMPGAN v3 substantially improves training stability and outperforms prior generative AMP models on external classifiers. We validated five candidates spanning three structural classes in vitro; two showed activity against Gram-positive strains, with the best candidate reaching MIC 8 μg/mL against B. subtilis. To support downstream curation, we further present PepCraft, a multi-agent framework for end-to-end AMP discovery in which a Planning Agent orchestrates specialized executors for generation, filtering, and verification. Its prioritization recommendations align with our in vitro outcomes.
Together, these contributions let us examine, on a small but real scale, how generative and agentic AI compose in therapeutic peptide discovery.
Code link: AMPGANv3 GitHub
Blogger's Review: AMPGAN v3 significantly enhances the design capabilities of antimicrobial peptides by introducing non-natural amino acids and chemical modifications, showcasing the potential of GANs in drug development. Its stable training process and impressive experimental results point to a promising future for antimicrobial drug discovery, warranting closer attention.