Title: MotifAgent: Motif-based Multi-Agent Graph-Language Alignment for Molecular Understanding and Generation
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: Multi-Agent Reinforcement Learning, Molecular Understanding, Large Language Models, Motif Assembly

Abstract: Large Language Models (LLMs) have shown great potential in molecular understanding by aligning molecular representations with text, enabling tasks like molecule captioning and property prediction to effectively capture molecular structures and predict functionalities. But existing approaches can only identify motifs without understanding their topological connection rules and assembly principles, preventing models from grasping the generative mechanisms of molecules. We introduce \textbf{MotifAgent}, a multi-agent reinforcement learning framework for molecular understanding. We formulate molecular assembly as a collaborative multi-agent problem, where each motif is represented by an agent sharing a common LLM backbone, dynamically reconstructing the molecule's 2D topology through global communication mechanisms. Our key innovations include: (1) inter-agent negotiation that models motif connections dynamically rather than statically; (2) a Set-based Behavioral Cloning mechanism that resolves assembly order ambiguity by learning multiple topologically equivalent paths; (3) Multi-Agent Proximal Policy Optimization (MAPPO) combined with topology-aware reward shaping to optimize target properties while maintaining chemical validity. Extensive experiments demonstrate that MotifAgent achieves substantial improvements on multiple molecule-text generation and molecular property prediction tasks, with our LLM-based generalist model surpassing or even reaching the state-of-the-art specialist models. Moreover, ablation experiments demonstrate that the MotifAgent multi-agent interaction framework can effectively learn molecular topological rules and generative principles.

TL;DR: We propose MotifAgent, a multi-agent reinforcement learning framework where LLM-based agents collaboratively assemble molecular motifs to learn connection rules and reconstruct 2D topology.