DeepGCGR: an interpretable two-layer deep learning model for the discovery of GCGR-activating compounds.
10.1016/S1875-5364(25)60969-1
- Author:
Xinyu TANG
1
;
Hongguo CHEN
2
;
Guiyang ZHANG
3
;
Huan LI
4
;
Danni ZHAO
1
;
Zenghao BI
4
;
Peng WANG
4
;
Jingwei ZHOU
4
;
Shilin CHEN
5
;
Zhaotong CONG
6
;
Wei CHEN
7
Author Information
1. School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.
2. State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078, China.
3. School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China; Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.
4. Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.
5. Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China; Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.
6. Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China; Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China. Electronic address: congzt@cdutcm.edu.cn.
7. Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China; Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China. Electronic address: greatchen@ncst.edu.cn.
- Publication Type:Journal Article
- Keywords:
Artificial intelligence;
Deep learning;
G protein-coupled receptor;
Natural products;
Type 2 diabetes mellitus
- MeSH:
Deep Learning;
Drug Discovery/methods*;
Humans;
Diabetes Mellitus, Type 2/metabolism*;
Medicine, Chinese Traditional;
Drugs, Chinese Herbal/pharmacology*
- From:
Chinese Journal of Natural Medicines (English Ed.)
2025;23(11):1301-1309
- CountryChina
- Language:English
-
Abstract:
The glucagon receptor (GCGR) is a critical target for the treatment of metabolic disorders such as Type 2 Diabetes Mellitus (T2DM) and obesity. Activation of GCGR enhances systemic insulin sensitivity through paracrine stimulation of insulin secretion, presenting a promising avenue for treatment. However, the discovery of effective GCGR agonists remains a challenging and resource-intensive process, often requiring time-consuming wet-lab experiments to synthesize and screen potential compounds. Recent advances in artificial intelligence technologies have demonstrated great potential in accelerating drug discovery by streamlining screening and efficiently predicting bioactivity. In the present work, we propose DeepGCGR, a two-layer deep learning model that leverages graph convolutional networks (GCN) integrated with a multiple attention mechanism to expedite the identification of GCGR agonists. In the first layer, the model predicts the bioactivity of various compounds against GCGR, efficiently filtering large chemical libraries to identify promising candidates. In the second layer, DeepGCGR classifies high bioactive compounds based on their functional effects on GCGR signaling, identifying those with potential agonistic or antagonistic effects. Moreover, DeepGCGR was specifically applied to identify novel GCGR-regulating compounds for the treatment of T2DM from natural products derived from traditional Chinese medicine (TCM). The proposed method will not only offer an effective strategy for discovering GCGR-targeting compounds with functional activation properties but also provide new insights into the development of T2DM therapeutics.
