Antimicrobial resistance causes to over a million deaths annually. Antimicrobial peptides (AMPs) are a promising solution, but generative AMP models are not yet 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: https://github.com/marszzibros/AMPGANv3

翻译：耐药性每年导致超百万人死亡。抗菌肽（AMP）是一种极具潜力的解决方案，然而现有生成式AMP模型尚无法设计含有非天然氨基酸和/或化学修饰的肽类，而这些修饰对实际肽类药物至关重要。我们提出AMPGAN v3——一种多目标条件生成对抗网络，其将生成词汇表扩展至D型氨基酸及酰胺化等N/C端修饰。通过两个特化判别器分离对抗监督与活性感知监督，AMPGAN v3显著提升了训练稳定性，并在外部分类器上优于既往生成式AMP模型。我们针对涵盖三个结构类别的五个候选物进行了体外验证：其中两个对革兰氏阳性菌株表现出活性，最佳候选物对枯草芽孢杆菌的最小抑菌浓度（MIC）达到8 μg/mL。为支持下游筛选，我们进一步提出PepCraft——一种用于端到端AMP发现的多智能体框架，其中规划智能体统筹协调生成、过滤与验证等特化执行模块。其优先级排序建议与我们的体外实验结果高度一致。综上，这些贡献使我们得以在虽小但真实的应用场景中，探究生成式AI与智能体AI在治疗性肽发现中的协同作用机制。代码地址：https://github.com/marszzibros/AMPGANv3