Mol-Debate: Multi-Agent Debate Improves Structural Reasoning in Molecular Design

2026-04-22

AI Summary

This work addresses the challenge of accurately translating natural language instructions into chemically valid molecular structures under stringent constraints in AI-driven drug discovery. The authors propose Mol-Debate, a novel framework that introduces a multi-agent debate mechanism to simulate the multi-perspective critique and iterative refinement characteristic of real-world drug design. Through a generate–debate–optimize loop, Mol-Debate uniquely coordinates developer and evaluator roles, integrating global and local structural reasoning with both static and dynamic chemical knowledge. Experimental results demonstrate that Mol-Debate achieves a 59.82% exact match rate on ChEBI-20 and a 50.52% weighted success rate on S²-Bench, substantially outperforming existing baselines.

Technology Category

• Multiagent Systems: Mechanism Design
• Machine Learning: Deep Neural Architectures and Foundation Models
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Text-guided molecular design is a key capability for AI-driven drug discovery, yet it remains challenging to map sequential natural-language instructions with non-linear molecular structures under strict chemical constraints. Most existing approaches, including RAG, CoT prompting, and fine-tuning or RL, emphasize a small set of ad-hoc reasoning perspectives implemented in a largely one-shot generation pipeline. In contrast, real-world drug discovery relies on dynamic, multi-perspective critique and iterative refinement to reconcile semantic intent with structural feasibility. Motivated by this, we propose Mol-Debate, a generation paradigm that enables such dynamic reasoning through an iterative generate-debate-refine loop. We further characterize key challenges in this paradigm and address them through perspective-oriented orchestration, including developer-debater conflict, global-local structural reasoning, and static-dynamic integration. Experiments demonstrate that Mol-Debate achieves state-of-the-art performance against strong general and chemical baselines, reaching 59.82% exact match on ChEBI-20 and 50.52% weighted success rate on S$^2$-Bench. Our code is available at https://github.com/wyuzh/Mol-Debate.

Problem

• molecular design
• text-guided generation
• structural reasoning
• chemical constraints
• AI-driven drug discovery

Innovation

• multi-agent debate
• iterative refinement
• structural reasoning
• molecular design
• perspective-oriented orchestration