TRACE: Transformation-Aware Graph Refinement for Reaction Condition Prediction

Identifying suitable reaction conditions is critical for chemical synthesis, as they directly affect yield, selectivity, and transformation feasibility. While recent methods have shown promising results, most approaches rely on static molecular representations and fail to explicitly capture the structural transformations between reactants and products, which are essential for understanding reaction mechanisms and predicting conditions. In this work, we propose TRACE, a transformation-aware graph refinement framework for reaction condition prediction. TRACE constructs an atom-level graph that captures reaction-specific structural changes by integrating information from both reactants and products. A structure-aware encoder enriches atom features with chemical context, followed by a dynamic interaction refinement module that adaptively selects transformation-relevant edges. A mechanism-regularized graph encoder further incorporates reaction center information to guide learning toward condition-relevant interactions, enabling the model to better capture reaction patterns for accurate condition prediction. Experiments on benchmark datasets show that TRACE achieves state-of-the-art performance across multiple condition types. The incorporation of transformation-aware refinement improves predictive accuracy, enhances generalization, and supports robust performance in realistic synthesis planning scenarios.