HiRes: Inspectable Precedent Memory for Reaction Condition Recommendation

Problem
This paper addresses the gap in the literature regarding the integration of predictive accuracy and interpretability in reaction condition recommendation systems. Specifically, it highlights the need for a model that not only provides accurate recommendations for catalysts, solvents, and reagents but also offers inspectable precedents that justify these recommendations. The work is presented as a preprint and has not yet undergone peer review.

Method
The authors introduce HiRes (Hierarchical Reaction Representations), a retrieval-augmented system designed for reaction condition recommendation. The architecture comprises several key components: a graph encoder that captures the structural information of chemical reactions, transformation-aware cross-attention mechanisms that facilitate the integration of different reaction features, and multi-stream reaction fusion that combines information from various sources. Additionally, a k-NN retrieval layer is employed to enhance the model’s ability to provide relevant precedents. The training process and specific compute resources are not disclosed, but the model is trained on a dataset derived from the USPTO database, focusing on primary-slot conditions.

Results
HiRes achieves state-of-the-art performance on the USPTO-Condition benchmark, with top-1 accuracies (Acc@1) of 0.929 for catalysts, 0.534 for solvents, and 0.530 for reagents. It ties with the best-reported baseline for catalyst recommendations while outperforming the REACON model for both solvent and reagent predictions. The authors conduct a paired bootstrap analysis, demonstrating statistically significant improvements in solvent and reagent selection when integrating retrieval mechanisms with learned condition heads, compared to purely parametric models.

Limitations
The authors acknowledge that while HiRes provides significant improvements in accuracy and interpretability, it may still be limited by the quality and diversity of the training data. The reliance on historical precedents could introduce biases based on the dataset’s composition. Additionally, the model’s performance on less common or novel reaction conditions is not thoroughly evaluated, which may limit its applicability in more exploratory chemical synthesis scenarios. The lack of peer review also raises questions about the robustness of the findings.

Why it matters
The development of HiRes has important implications for the field of computational chemistry and machine learning applications in synthetic planning. By bridging the gap between predictive accuracy and interpretability, HiRes not only enhances the reliability of reaction condition recommendations but also provides chemists with the necessary context to understand and trust these recommendations. This dual capability could facilitate more efficient and informed decision-making in chemical synthesis, potentially accelerating the pace of discovery in drug development and materials science.

Authors: Shreyas Vinaya Sathyanarayana, Raja Sekhar Pappala, Deepak Warrier
Source: arXiv:2605.21420
URL: https://arxiv.org/abs/2605.21420v1