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Proceedings of the AAAI Conference on Artificial Intelligence

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TRACE: Transformation-Aware Graph Refinement for Reaction...
Yujie Chen, · 2026-03-14 · via Proceedings of the AAAI Conference on Artificial Intelligence

Authors

  • Yujie Chen State Key Laboratory of Chemo and Biosensing, College of Computer Science and Electronic Engineering, Hunan University The Ministry of Education Key Laboratory of Fusion Computing of Supercomputing and Artificial Intelligence, Hunan University
  • Tengfei Ma State Key Laboratory of Chemo and Biosensing, College of Computer Science and Electronic Engineering, Hunan University The Ministry of Education Key Laboratory of Fusion Computing of Supercomputing and Artificial Intelligence, Hunan University
  • Yuansheng Liu State Key Laboratory of Chemo and Biosensing, College of Computer Science and Electronic Engineering, Hunan University The Ministry of Education Key Laboratory of Fusion Computing of Supercomputing and Artificial Intelligence, Hunan University
  • Leyi Wei Centre for Artificial Intelligence driven Drug Discovery, Faculty of Applied Science, Macao Polytechnic University
  • Shu Wu NLPR, MAIS, Institute of Automation, Chinese Academy of Sciences
  • Dongsheng Cao Xiangya School of Pharmaceutical Sciences, Central South University
  • Yiping Liu State Key Laboratory of Chemo and Biosensing, College of Computer Science and Electronic Engineering, Hunan University The Ministry of Education Key Laboratory of Fusion Computing of Supercomputing and Artificial Intelligence, Hunan University
  • Xiangxiang Zeng State Key Laboratory of Chemo and Biosensing, College of Computer Science and Electronic Engineering, Hunan University The Ministry of Education Key Laboratory of Fusion Computing of Supercomputing and Artificial Intelligence, Hunan University

DOI:

https://doi.org/10.1609/aaai.v40i1.36971

Abstract

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.

How to Cite

Chen, Y., Ma, T., Liu, Y., Wei, L., Wu, S., Cao, D., … Zeng, X. (2026). TRACE: Transformation-Aware Graph Refinement for Reaction Condition Prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 119–127. https://doi.org/10.1609/aaai.v40i1.36971

Issue

Section

AAAI Technical Track on Application Domains I

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