MAC-AMP: A Closed-Loop Multi-Agent Collaboration System for Multi-Objective Antimicrobial Peptide Design

TL;DR

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.

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 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.

Figures (14)

• Figure 1: MAC-AMP Framework. (a) Overview of the closed-loop workflow that iteratively guides AMP design from input to output. (b) Schematic of the MAC-AMP pipeline, showing its modules and their interactions.
• Figure 2: Overview of the Artificial Intelligence-simulated Peer Review module. Green text indicates the injectable section, while the green boxes denote content obtained from the separate preparatory meeting.
• Figure 3: Overview of the Reinforcement Learning Refinement module.
• Figure 4: (a) UMAP of the peptide chemical space for Escherichia coli (E. coli) inhibition. Gray dots show UniProt peptides, orange dots show real-world E. coli AMPs, and red dots are MAC-AMP candidate AMPs. Nested blue contours indicate increasing kernel density from these AMPs. (b,c) Sequence diagrams of two example MAC-AMP designed E. coli AMPs.
• Figure A1: Three-stage reward–episode learning curves for MAC-AMP. The curves illustrate how the total reward and its components ($S_a$, antibacterial activity score; $S_b$, AMP likelihood score; and $S_c$, average meta score) evolve during (a) the early stage, (b) the middle stage, and (c) the late stage. The light blue area indicates the moving variance of the total reward.