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[2602.14926] MAC-AMP: A Closed-Loop Multi-Agent Collaboration System for Multi-Objective Antimicrobial Peptide Design
Summary
The article presents MAC-AMP, a novel closed-loop multi-agent system designed for the multi-objective optimization of antimicrobial peptides, addressing challenges in antimicrobial resistance.
Why It Matters
With the rise of antimicrobial resistance, innovative solutions like MAC-AMP are crucial for developing effective antimicrobial peptides. This system leverages AI to optimize peptide design, balancing activity, toxicity, and novelty, which is vital for advancing healthcare solutions.
Key Takeaways
- MAC-AMP utilizes a closed-loop multi-agent collaboration system for peptide design.
- The system supports multi-objective optimization, improving antibacterial activity and toxicity compliance.
- MAC-AMP outperforms existing AMP generative models by optimizing key molecular properties.
Computer Science > Artificial Intelligence arXiv:2602.14926 (cs) [Submitted on 16 Feb 2026] Title:MAC-AMP: A Closed-Loop Multi-Agent Collaboration System for Multi-Objective Antimicrobial Peptide Design Authors:Gen Zhou, Sugitha Janarthanan, Lianghong Chen, Pingzhao Hu View a PDF of the paper titled MAC-AMP: A Closed-Loop Multi-Agent Collaboration System for Multi-Objective Antimicrobial Peptide Design, by Gen Zhou and 3 other authors View PDF HTML (experimental) Abstract: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 l...
