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.