Reaction Graph: Towards Reaction-Level Modeling for Chemical Reactions with 3D Structures

Yingzhao Jian, Yue Zhang, Ying Wei, Hehe Fan, Yi Yang
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:27426-27491, 2025.

Abstract

Accurately modeling chemical reactions using Artificial Intelligence (AI) can accelerate discovery and development, especially in fields like drug design and material science. Although AI has made remarkable advancements in single molecule recognition, such as predicting molecular properties, the study of interactions between molecules, particularly chemical reactions, has been relatively overlooked. In this paper, we introduce Reaction Graph (RG), a unified graph representation that encapsulates the 3D molecular structures within chemical reactions. RG integrates the molecular graphs of reactants and products into a cohesive framework, effectively capturing the interatomic relationships pertinent to the reaction process. Additionally, it incorporates the 3D structure information of molecules in a simple yet effective manner. We conduct experiments on a range of tasks, including chemical reaction classification, condition prediction, and yield prediction. RG achieves the highest accuracy across six datasets, demonstrating its effectiveness. The code is available at https://github.com/Shadow-Dream/Reaction-Graph.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-jian25b, title = {Reaction Graph: Towards Reaction-Level Modeling for Chemical Reactions with 3{D} Structures}, author = {Jian, Yingzhao and Zhang, Yue and Wei, Ying and Fan, Hehe and Yang, Yi}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {27426--27491}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/jian25b/jian25b.pdf}, url = {https://proceedings.mlr.press/v267/jian25b.html}, abstract = {Accurately modeling chemical reactions using Artificial Intelligence (AI) can accelerate discovery and development, especially in fields like drug design and material science. Although AI has made remarkable advancements in single molecule recognition, such as predicting molecular properties, the study of interactions between molecules, particularly chemical reactions, has been relatively overlooked. In this paper, we introduce Reaction Graph (RG), a unified graph representation that encapsulates the 3D molecular structures within chemical reactions. RG integrates the molecular graphs of reactants and products into a cohesive framework, effectively capturing the interatomic relationships pertinent to the reaction process. Additionally, it incorporates the 3D structure information of molecules in a simple yet effective manner. We conduct experiments on a range of tasks, including chemical reaction classification, condition prediction, and yield prediction. RG achieves the highest accuracy across six datasets, demonstrating its effectiveness. The code is available at https://github.com/Shadow-Dream/Reaction-Graph.} }
Endnote
%0 Conference Paper %T Reaction Graph: Towards Reaction-Level Modeling for Chemical Reactions with 3D Structures %A Yingzhao Jian %A Yue Zhang %A Ying Wei %A Hehe Fan %A Yi Yang %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-jian25b %I PMLR %P 27426--27491 %U https://proceedings.mlr.press/v267/jian25b.html %V 267 %X Accurately modeling chemical reactions using Artificial Intelligence (AI) can accelerate discovery and development, especially in fields like drug design and material science. Although AI has made remarkable advancements in single molecule recognition, such as predicting molecular properties, the study of interactions between molecules, particularly chemical reactions, has been relatively overlooked. In this paper, we introduce Reaction Graph (RG), a unified graph representation that encapsulates the 3D molecular structures within chemical reactions. RG integrates the molecular graphs of reactants and products into a cohesive framework, effectively capturing the interatomic relationships pertinent to the reaction process. Additionally, it incorporates the 3D structure information of molecules in a simple yet effective manner. We conduct experiments on a range of tasks, including chemical reaction classification, condition prediction, and yield prediction. RG achieves the highest accuracy across six datasets, demonstrating its effectiveness. The code is available at https://github.com/Shadow-Dream/Reaction-Graph.
APA
Jian, Y., Zhang, Y., Wei, Y., Fan, H. & Yang, Y.. (2025). Reaction Graph: Towards Reaction-Level Modeling for Chemical Reactions with 3D Structures. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:27426-27491 Available from https://proceedings.mlr.press/v267/jian25b.html.

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