@article{0cb8b596e70d410490df77a9c6f56766,
title = "Interpretable prediction of aggregation-induced emission molecules based on graph neural networks",
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.",
author = "Zhang, \{Shi Chen\} and Jun Zhu and Yi Zeng and Mai, \{Hua Qi\} and Dong Wang and Zheng, \{Xiao Yan\}",
note = "Publisher Copyright: {\textcopyright} 2025 The Royal Society of Chemistry.",
year = "2025",
month = may,
day = "14",
doi = "10.1039/d5cc01949d",
language = "English",
volume = "61",
pages = "8899--8902",
journal = "Chemical Communications",
issn = "1359-7345",
publisher = "Royal Society of Chemistry",
number = "49",
}