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

Shi Chen Zhang; Jun Zhu; Yi Zeng; Hua Qi Mai; Dong Wang; Xiao Yan Zheng

doi:10.1039/d5cc01949d

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

Shi Chen Zhang
, Jun Zhu
, Yi Zeng
, Hua Qi Mai
, Dong Wang^*
, Xiao Yan Zheng^*

^*此作品的通讯作者

Beijing Institute of Technology
Shenzhen University

科研成果: 期刊稿件 › 文章 › 同行评审

2 引用（Scopus）

摘要

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.

源语言	英语
页（从-至）	8899-8902
页数	4
期刊	Chemical Communications
卷	61
期	49
DOI	https://doi.org/10.1039/d5cc01949d
出版状态	已出版 - 14 5月 2025
已对外发布	是

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10.1039/d5cc01949d

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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",

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doi = "10.1039/d5cc01949d",

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T1 - Interpretable prediction of aggregation-induced emission molecules based on graph neural networks

AU - Zhang, Shi Chen

AU - Zhu, Jun

AU - Zeng, Yi

AU - Mai, Hua Qi

AU - Wang, Dong

AU - Zheng, Xiao Yan

PY - 2025/5/14

Y1 - 2025/5/14

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/105005891963

U2 - 10.1039/d5cc01949d

DO - 10.1039/d5cc01949d

M3 - Article

C2 - 40395042

AN - SCOPUS:105005891963

SN - 1359-7345

VL - 61

SP - 8899

EP - 8902

JO - Chemical Communications

JF - Chemical Communications

IS - 49

ER -

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

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