To address the global health threat of antimicrobial resistance, antimicrobial peptides (AMP) are being explored for their potent and promising ability to fight resistant pathogens. While artificial intelligence (AI) is being employed to advance AMP discovery and design, most AMP design models struggle to balance key goals like activity, toxicity, and novelty, using rigid or unclear scoring methods that make results hard to interpret and optimize. As the capabilities of Large Language Models (LLM) advance and evolve swiftly, we turn to AI multi-agent collaboration based on such models (multi-agent LLMs), which show rapidly rising potential in complex scientific design scenarios. Based on this, we introduce MAC-AMP, a closed-loop multi-agent collaboration (MAC) system for multi-objective AMP design. The system implements a fully autonomous simulated peer review-adaptive reinforcement learning framework that requires only a task description and example dataset to design novel AMPs. The novelty of our work lies in introducing a closed-loop multi-agent system for AMP design, with cross-domain transferability, that supports multi-objective optimization while remaining explainable rather than a 'black box'. Experiments show that MAC-AMP outperforms other AMP generative models by effectively optimizing AMP generation for multiple key molecular properties, demonstrating exceptional results in antibacterial activity, AMP likeliness, toxicity compliance, and structural reliability.

翻译：为应对全球性的抗菌素耐药性健康威胁，抗菌肽（AMP）因其对抗耐药病原体的强大潜力而备受关注。虽然人工智能（AI）正被用于推进抗菌肽的发现与设计，但大多数抗菌肽设计模型难以平衡活性、毒性和新颖性等关键目标，且通常采用僵化或不明确的评分方法，导致结果难以解释和优化。随着大语言模型（LLM）能力的快速演进，我们转向基于此类模型的多智能体协作（multi-agent LLMs），其在复杂科学设计场景中展现出迅速增长的潜力。基于此，我们提出了MAC-AMP，一种用于多目标抗菌肽设计的闭环多智能体协作系统。该系统实现了一个完全自主的模拟同行评审-自适应强化学习框架，仅需任务描述和示例数据集即可设计新型抗菌肽。本工作的创新之处在于引入了一种具有跨领域可迁移性的闭环多智能体系统，用于抗菌肽设计，该系统支持多目标优化，同时保持可解释性而非“黑箱”。实验表明，MAC-AMP通过有效优化多个关键分子特性来生成抗菌肽，其性能优于其他抗菌肽生成模型，在抗菌活性、抗菌肽相似性、毒性合规性和结构可靠性方面均表现出优异结果。