用AI生成含非天然氨基酸的抗菌肽，突破传统设计局限。
Agentic Discovery of Non-Canonical Antimicrobial Peptides with AMPGAN v3
- 采用双判别器分离对抗训练与活性监督，提升生成稳定性。
- 生成候选肽在体外对革兰氏阳性菌有效，最低抑菌浓度达8 μg/mL。
- 构建多智能体框架实现从设计到验证的全流程自动化发现。
抗菌耐药每年导致逾百万死亡。抗菌肽（AMPs）是潜在解决方案，但现有生成模型无法设计含非天然氨基酸或化学修饰的肽，而这类修饰对药物实用性至关重要。本文提出AMPGAN v3，一种多目标条件GAN，将生成词汇扩展至D-氨基酸及N/C端修饰（如酰胺化）。通过两个专用判别器分别处理对抗性与活性感知监督，显著提升训练稳定性，并在外部分类器上优于先前生成模型。我们体外验证了五个跨越三类结构的候选肽，其中两个对革兰氏阳性菌具活性，最佳候选物对B. subtilis的最小抑菌浓度（MIC）为8 μg/mL。为进一步支持下游筛选，我们提出PepCraft——一个用于端到端抗菌肽发现的多智能体框架，由规划代理协调生成、过滤与验证执行器。其优先推荐与实验结果高度一致。这些成果使我们首次在小规模真实场景下探索生成式与代理式AI在治疗肽发现中的协同作用。代码：https://github.com/marszzibros/AMPGANv3
原文摘要 · Abstract (English)
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
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