构建多目标逆合成规划的智能体框架，提升安全、成本与效率平衡。
MMORF: A Multi-agent Framework for Designing Multi-objective Retrosynthesis Planning Systems
- 设计模块化智能体系统，灵活组合实现多目标协同决策。
- 在218个任务上，硬约束任务成功率超48.6%，优于现有方法。
- 适合化学合成设计与AI辅助研发人员参考使用。
多目标逆合成规划是化学领域关键任务，需动态权衡质量、安全与成本。基于语言模型的多智能体系统（MAS）通过专业化智能体协作，为该任务提供了可行方案。本文提出MMORF框架，支持模块化智能体组件的灵活配置，可构建多种系统并进行系统性评估。基于该框架，我们构建了两个代表性系统：MASIL与RFAS。在新构建的包含218个多目标逆合成规划任务的基准上，MASIL在软约束任务中表现优异，频繁生成帕累托最优路线；而RFAS在硬约束任务中达到48.6%的成功率，超越当前最优基线。结果表明，MMORF是探索多智能体系统用于多目标逆合成规划的有效基础框架。代码与数据见 https://anonymous.4open.science/r/MMORF/。
原文摘要 · Abstract (English)
Multi-objective retrosynthesis planning is a critical chemistry task requiring dynamic balancing of quality, safety, and cost objectives. Language model-based multi-agent systems (MAS) offer a promising approach for this task: leveraging interactions of specialized agents to incorporate multiple objectives into retrosynthesis planning. We present MMORF, a framework for constructing MAS for multi-objective retrosynthesis planning. MMORF features modular agentic components, which can be flexibly combined and configured into different systems, enabling principled evaluation and comparison of different system designs. Using MMORF, we construct two representative MAS: MASIL and RFAS. On a newly curated benchmark consisting of 218 multi-objective retrosynthesis planning tasks, MASIL achieves strong safety and cost metrics on soft-constraint tasks, frequently Pareto-dominating baseline routes, while RFAS achieves a 48.6% success rate on hard-constraint tasks, outperforming state-of-the-art baselines. Together, these results show the effectiveness of MMORF as a foundational framework for exploring MAS for multi-objective retrosynthesis planning. Code and data are available at https://anonymous.4open.science/r/MMORF/.
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