RetroReasoner: A Reasoning LLM for Strategic Retrosynthesis Prediction
arXiv:2603.12666v1 Announce Type: new Abstract: Retrosynthesis prediction is a core task in organic synthesis that aims to predict reactants for a given product molecule. Traditionally, chemists select a plausible bond disconnection and derive corresponding reactants, which is time-consuming and requires substantial expertise. While recent advancements in molecular large language models (LLMs) have made progress, many methods either predict reactants without strategic reasoning or conduct only a generic product analysis, rather than reason explicitly about bond-disconnection strategies that logically lead to the choice of specific reactants. To overcome these limitations, we propose RetroReasoner, a retrosynthetic reasoning model that leverages chemists' strategic thinking. RetroReasoner is trained using both supervised fine-tuning (SFT) and reinforcement learning (RL). For SFT, we introduce SyntheticRetro, a framework that generates structured disconnection rationales alongside react
arXiv:2603.12666v1 Announce Type: new Abstract: Retrosynthesis prediction is a core task in organic synthesis that aims to predict reactants for a given product molecule. Traditionally, chemists select a plausible bond disconnection and derive corresponding reactants, which is time-consuming and requires substantial expertise. While recent advancements in molecular large language models (LLMs) have made progress, many methods either predict reactants without strategic reasoning or conduct only a generic product analysis, rather than reason explicitly about bond-disconnection strategies that logically lead to the choice of specific reactants. To overcome these limitations, we propose RetroReasoner, a retrosynthetic reasoning model that leverages chemists' strategic thinking. RetroReasoner is trained using both supervised fine-tuning (SFT) and reinforcement learning (RL). For SFT, we introduce SyntheticRetro, a framework that generates structured disconnection rationales alongside reactant predictions. In the case of RL, we apply a round-trip accuracy as reward, where predicted reactants are passed through a forward synthesis model, and predictions are rewarded when the forward-predicted product matches the original input product. Experimental results show that RetroReasoner not only outperforms prior baselines but also generates a broader range of feasible reactant proposals, particularly in handling more challenging reaction instances.
Executive Summary
The article introduces RetroReasoner, a reasoning large language model (LLM) that improves upon existing methods in retrosynthesis prediction by leveraging chemists' strategic thinking. The model combines supervised fine-tuning (SFT) and reinforcement learning (RL) to generate structured disconnection rationales alongside reactant predictions. Experimental results show that RetroReasoner outperforms prior baselines and generates a broader range of feasible reactant proposals. This advancement has significant implications for the field of organic synthesis, as it can aid chemists in identifying plausible bond disconnections and selecting corresponding reactants. The model's ability to reason strategically about bond-disconnection strategies makes it a valuable tool for predicting reactants and improving the efficiency of the retrosynthesis process.
Key Points
- ▸ RetroReasoner combines SFT and RL to improve retrosynthesis prediction
- ▸ The model generates structured disconnection rationales alongside reactant predictions
- ▸ RetroReasoner outperforms prior baselines and generates a broader range of feasible reactant proposals
Merits
Strategic Reasoning
RetroReasoner leverages chemists' strategic thinking to improve retrosynthesis prediction, making it a more effective tool for predicting reactants and improving the efficiency of the retrosynthesis process.
Improved Performance
Experiments show that RetroReasoner outperforms prior baselines and generates a broader range of feasible reactant proposals, particularly in handling more challenging reaction instances.
Demerits
Complexity
The combination of SFT and RL may increase the complexity of the model and make it more difficult to interpret and analyze its predictions.
Data Requirements
The model requires a large dataset of labeled examples and may require significant computational resources and expertise to train and deploy.
Expert Commentary
The introduction of RetroReasoner represents a significant advancement in the field of retrosynthesis prediction. By leveraging chemists' strategic thinking and combining SFT and RL, the model is able to generate structured disconnection rationales alongside reactant predictions and outperform prior baselines. While the complexity of the model and data requirements may be limitations, the potential benefits of RetroReasoner make it an exciting development in the field. As the field of organic synthesis continues to evolve, it will be interesting to see how models like RetroReasoner are integrated into the workflow of chemists and the impact they have on the efficiency and effectiveness of the retrosynthesis process.
Recommendations
- ✓ Further research should be conducted to explore the limitations and potential applications of RetroReasoner in various domains of organic synthesis.
- ✓ The development of more interpretable and transparent models, such as those using explainable AI techniques, may be necessary to facilitate the adoption of models like RetroReasoner in the field.