Interpretable prediction of aggregation-induced emission molecules based on graph neural networks.

Journal: Chemical communications (Cambridge, England)
Published Date: Jun 12, 2025

Abstract

We developed an interpretable graph neural network (96.4% accuracy) for AIEgen identification, revealing 24 characteristic functional groups. Based on these insights, two virtual library strategies (self-fragment and donor-acceptor docking) were proposed and predicted four experimentally confirmed AIEgens successfully, which establishes a rational design framework for AIE materials.

Authors

  • Shi-Chen Zhang
    Key Laboratory of Cluster Science of Ministry of Education, Key Laboratory of Medicinal Molecule Science and Pharmaceutics Engineering of Ministry of Industry and Information Technology, Beijing Key Laboratory of Photoelectronic/Electro-photonic Conversion Materials, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing 100081, P. R. China. xiaoyanzheng@bit.edu.cn.
  • Jun Zhu
    Sinopharm Dongfeng General Hospital, Hubei University of Medicine, Shiyan, Hubei, 442008, China.
  • Yi Zeng
    Department of Geriatrics, Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, China.
  • Hua-Qi Mai
    Key Laboratory of Cluster Science of Ministry of Education, Key Laboratory of Medicinal Molecule Science and Pharmaceutics Engineering of Ministry of Industry and Information Technology, Beijing Key Laboratory of Photoelectronic/Electro-photonic Conversion Materials, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing 100081, P. R. China. xiaoyanzheng@bit.edu.cn.
  • Dong Wang
    Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
  • Xiao-Yan Zheng
    Key Laboratory of Cluster Science of Ministry of Education, Key Laboratory of Medicinal Molecule Science and Pharmaceutics Engineering of Ministry of Industry and Information Technology, Beijing Key Laboratory of Photoelectronic/Electro-photonic Conversion Materials, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing 100081, P. R. China. xiaoyanzheng@bit.edu.cn.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals