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 either encode reactants and products independently or rely on rule-based reaction graphs, both of which constrain the ability of the model to capture condition-relevant structural transformations. In this work, we propose TRACE, a transformation-aware graph refinement framework for reaction condition prediction. TRACE constructs atom-level joint graphs that integrate both reactant and product structures to represent condition-relevant transformations. A structure-aware encoder enriches atom features with local chemical context, followed by a dynamic interaction refinement module that adaptively infers task-specific edges. To further guide the model toward condition-relevant patterns, a mechanism regularized graph encoder incorporates reaction center information, enabling more accurate modeling of transformation mechanisms. Experiments on benchmark datasets show that TRACE achieves state-of-the-art performance across multiple condition types. The integration of transformation-aware refinement leads to improvements in prediction accuracy and generalization, while maintaining robust performance in challenging and realistic synthesis planning scenarios.

Authors

• Yujie Chen Shenzhen University
• Tengfei Ma College of Computer Science and Electronic Engineering, Hunan University
• Yuansheng Liu Hunan University
• Leyi Wei Centre for Artificial Intelligence driven Drug Discovery, Faculty of Applied Science, Macao Polytechnic University
• Shu Wu Center for Research on Intelligent Perception and Computing, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences School of Artificial Intelligence, University of Chinese Academy of Sciences
• Dongsheng Cao Xiangya School of Pharmaceutical Sciences, Central South University
• Yiping Liu Hunan University
• Xiangxiang Zeng

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