TMCS: Tool-Grounded Multi-Agent Reasoning for Compositional Chemical Problem Solving

2026-09-28

AI Summary

This study addresses the challenge that large language models (LLMs) face in handling quantitatively constrained molecular modifications and systematic revisions within combinatorial chemistry. To this end, we propose TMCS, a framework that formalizes chemical problem-solving as an interpretable, tool-augmented workflow. By leveraging multi-agent collaboration, TMCS unifies the stages of molecular generation, understanding, and editing. Furthermore, it incorporates few-shot trajectory memory and structured reflection mechanisms to enable closed-loop iterative optimization. Experimental results demonstrate that TMCS substantially enhances model performance across diverse chemical reasoning tasks, achieving state-of-the-art results on both open-source and proprietary LLMs.

Abstract

Despite the promise of Large Language Models (LLMs) in computational chemistry, rigorous combinatorial chemistry problems remain difficult because they require quantitatively constrained molecular modification, candidate validation, and systematic revision after failed attempts. Existing tool-augmented chemical agents demonstrate useful planning and tool use, but they rarely provide a unified loop for property-driven molecular optimization and workflow-level composition. To bridge this gap, we propose Tool-Grounded Multi-Agent Reasoning for Compositional Chemical Problem Solving (TMCS), a step-by-step multi-agent framework that formalizes chemical problem solving as an interpretable, tool-augmented workflow. At the task level, specialized agents leverage external tools, few-shot trajectory memory, and structured reflection to iteratively refine solutions. At the workflow level, TMCS chains generation, understanding, editing, description, and optimization into a closed-loop pipeline. Evaluations across multiple chemical tasks demonstrate that TMCS consistently enhances chemical reasoning across both open- and closed-source base models, achieving state-of-the-art performance.

Problem

• Combinatorial Chemistry
• Molecular Optimization
• Tool-Augmented Agents
• Multi-Agent Reasoning
• Closed-Loop Workflow

Innovation

• Multi-Agent Reasoning
• Tool-Augmented Workflow
• Compositional Chemistry
• Closed-Loop Pipeline
• Structured Reflection