Reinforced Reasoning for End-to-End Retrosynthetic Planning
Summary: arXiv:2603.29723v1 Announce Type: new
Abstract
Retrosynthetic planning is a fundamental task in organic chemistry, yet remains challenging due to its combinatorial complexity. To address this, conventional approaches typically rely on hybrid frameworks that combine single-step predictions with external search heuristics, inevitably fracturing the logical coherence between local molecular transformations and global planning objectives.
Introduction
In the field of organic chemistry, retrosynthetic planning is crucial for designing synthetic pathways to create complex molecules. However, the inherent complexity of this task presents significant challenges, primarily due to the vast number of possible molecular transformations. Traditional methods have often struggled to maintain a cohesive and logical approach throughout the planning process.
Challenges in Conventional Approaches
Conventional frameworks for retrosynthetic planning generally take a hybrid approach, integrating single-step predictions with heuristic-based searches. While this method offers some advantages, it tends to compromise the logical connections between individual transformation steps and the overarching synthetic goals. This disconnect can lead to inefficiencies and suboptimal planning outcomes.
Introducing ReTriP
To overcome these limitations, we present ReTriP (Retrosynthetic Transformation Planning), an innovative end-to-end generative framework that reformulates the retrosynthesis process as a direct Chain-of-Thought reasoning task. This novel approach aims to enhance the logical flow of reasoning in retrosynthetic planning by embedding strategic foresight directly into the model’s chemical reasoning capabilities.
Methodology
ReTriP establishes a path-coherent molecular representation that facilitates a seamless connection between individual transformation steps and overall planning objectives. Our methodology employs a progressive training curriculum that transitions from reasoning distillation to reinforcement learning, utilizing verifiable rewards to ensure alignment between stepwise generation and practical route utility.
Empirical Evaluation
To assess the effectiveness of ReTriP, we conducted extensive evaluations using the RetroBench benchmark. The results demonstrate that ReTriP achieves state-of-the-art performance in retrosynthetic planning tasks, showcasing superior robustness, particularly in long-horizon planning scenarios when compared to traditional hybrid baselines.
Conclusion
The introduction of ReTriP marks a significant advancement in the field of retrosynthetic planning. By reframing the problem as a Chain-of-Thought reasoning task and employing a robust training methodology, we have bridged the gap between local transformations and global objectives. This innovation not only enhances the logical coherence of synthetic planning but also sets a new benchmark for performance in the domain.
Future Work
Moving forward, we aim to refine the ReTriP framework further and explore its application across various domains within organic chemistry. Continued research will focus on optimizing the model’s reasoning capabilities and expanding its utility in complex synthetic scenarios.