A graph-convolutional neural network for addressing small-scale reaction prediction.

Journal: Chemical communications (Cambridge, England)
Published Date: Apr 27, 2021

Abstract

We describe a graph-convolutional neural network (GCN) model, the reaction prediction capabilities of which are as potent as those of the transformer model based on sufficient data, and we adopt the Baeyer-Villiger oxidation reaction to explore their performance differences based on limited data. The top-1 accuracy of the GCN model (90.4%) is higher than that of the transformer model (58.4%).

Authors

  • Yejian Wu
    Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China. hduan@zjut.edu.cn.
  • Chengyun Zhang
    Artificial Intelligent Aided Drug Discovery Lab, College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, China.
  • Ling Wang
    The State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, #7 Jinsui Road, Guangzhou, Guangdong 510230, China.
  • Hongliang Duan
    Artificial Intelligent Aided Drug Discovery Lab, College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, China.

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