MMORF: A Multi-agent Framework for Designing Multi-objective Retrosynthesis Planning Systems
arXiv:2604.05075v1 Announce Type: new Abstract: 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-co
arXiv:2604.05075v1 Announce Type: new Abstract: 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/.
Executive Summary
The article introduces MMORF, a modular multi-agent framework for multi-objective retrosynthesis planning in chemistry. Leveraging language model-based multi-agent systems (MAS), MMORF enables dynamic balancing of quality, safety, and cost objectives through specialized, configurable agents. The framework is demonstrated via two systems—MASIL and RFAS—evaluated on a 218-task benchmark. MASIL excels in soft-constraint tasks, frequently Pareto-dominating baselines, while RFAS achieves a 48.6% success rate on hard-constraint tasks, surpassing state-of-the-art methods. The framework’s modularity and benchmarking capabilities position it as a foundational tool for advancing MAS in retrosynthesis planning, with code and data publicly available for further research.
Key Points
- ▸ MMORF is a modular framework for constructing multi-agent systems (MAS) tailored to multi-objective retrosynthesis planning, addressing trade-offs between quality, safety, and cost.
- ▸ Two representative systems, MASIL and RFAS, are developed using MMORF, demonstrating superior performance on soft- and hard-constraint tasks, respectively.
- ▸ The framework introduces a curated benchmark of 218 multi-objective retrosynthesis tasks, enabling principled evaluation and comparison of MAS designs.
Merits
Modularity and Flexibility
MMORF’s modular agentic components allow for flexible configuration and recombination, facilitating systematic exploration of MAS architectures and enabling principled comparisons between designs.
Benchmarking Advancement
The introduction of a dedicated benchmark of 218 multi-objective tasks provides a standardized platform for evaluating and advancing retrosynthesis planning systems, addressing a critical gap in the field.
Performance Superiority
MASIL and RFAS demonstrate state-of-the-art performance in their respective domains, with MASIL achieving Pareto-optimal results in soft-constraint tasks and RFAS outperforming baselines in hard-constraint scenarios, validating the framework’s efficacy.
Demerits
Limited Benchmark Scope
The benchmark’s size (218 tasks) may be insufficient for comprehensive evaluation, particularly in capturing the diversity of real-world retrosynthesis challenges across chemical domains and objectives.
Hard-Constraint Performance Gap
While RFAS achieves a 48.6% success rate on hard-constraint tasks, this still leaves significant room for improvement, highlighting the inherent difficulty of balancing rigid constraints in retrosynthesis planning.
Agent Specialization Dependency
The framework’s effectiveness is contingent on the quality and specialization of its agents; suboptimal agent design or configuration could undermine the system’s performance, despite the framework’s modularity.
Expert Commentary
MMORF represents a significant advancement in the application of multi-agent systems to retrosynthesis planning, a domain where traditional computational methods often struggle to balance competing objectives. The framework’s modular design is particularly noteworthy, as it decouples the system’s architecture from its performance, allowing for systematic exploration of agent interactions and configurations. This approach not only facilitates benchmarking but also lays the groundwork for adaptive, self-optimizing systems. The empirical results—particularly the performance of RFAS on hard-constraint tasks—are impressive and suggest that MAS can effectively tackle complex, real-world chemistry problems. However, the framework’s long-term success will depend on its adoption by the broader research community and its ability to scale beyond the current benchmark. Future work should explore the integration of MMORF with experimental automation, as well as the development of more sophisticated agent collaboration protocols to further enhance performance. The framework’s open-source nature is commendable and aligns with the growing emphasis on reproducibility in AI research.
Recommendations
- ✓ Expand the benchmark dataset to include a wider variety of chemical structures and objective functions (e.g., environmental impact, scalability) to better reflect real-world scenarios and enhance the framework’s robustness.
- ✓ Investigate hybrid approaches that combine MMORF with traditional retrosynthesis methods (e.g., template-based or template-free models) to leverage the strengths of both paradigms and improve overall system performance.
- ✓ Develop standardized evaluation metrics for multi-objective retrosynthesis that account for trade-offs between objectives, enabling more nuanced comparisons between systems.
- ✓ Explore the integration of MMORF with robotic process automation and closed-loop experimental platforms to bridge the gap between computational planning and laboratory execution, paving the way for fully autonomous chemistry systems.
- ✓ Conduct user studies and workshops with chemists and AI researchers to gather feedback on the framework’s usability, identify pain points, and iteratively improve the design of agentic components.
Sources
Original: arXiv - cs.AI