Artificial intelligence-enabled discovery of a RIPK3 inhibitor with neuroprotective effects in an acute glaucoma mouse model.
10.1097/CM9.0000000000003387
- Author:
Xing TU
1
;
Zixing ZOU
2
;
Jiahui LI
2
;
Simiao ZENG
2
;
Zhengchao LUO
3
;
Gen LI
4
;
Yuanxu GAO
2
;
Kang ZHANG
2
Author Information
1. Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, China.
2. Guangzhou National Laboratory, Guangzhou International Bio Island, Guangzhou, Guangdong 510530, China.
3. Department of Big Data and Biomedical AI, College of Future Technology, Peking University, Beijing 100871, China.
4. Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, Guangdong 510623, China.
- Publication Type:Journal Article
- MeSH:
Animals;
Glaucoma/metabolism*;
Neuroprotective Agents/pharmacology*;
Mice;
Receptor-Interacting Protein Serine-Threonine Kinases/metabolism*;
Artificial Intelligence;
Retinal Ganglion Cells/metabolism*;
Disease Models, Animal;
Molecular Docking Simulation;
Mice, Inbred C57BL;
Male
- From:
Chinese Medical Journal
2025;138(2):172-184
- CountryChina
- Language:English
-
Abstract:
BACKGROUND:Retinal ganglion cell (RGC) death caused by acute ocular hypertension is an important characteristic of acute glaucoma. Receptor-interacting protein kinase 3 (RIPK3) that mediates necroptosis is a potential therapeutic target for RGC death. However, the current understanding of the targeting agents and mechanisms of RIPK3 in the treatment of glaucoma remains limited. Notably, artificial intelligence (AI) technologies have significantly advanced drug discovery. This study aimed to discover RIPK3 inhibitor with AI assistance.
METHODS:An acute ocular hypertension model was used to simulate pathological ocular hypertension in vivo . We employed a series of AI methods, including large language and graph neural network models, to identify the target compounds of RIPK3. Subsequently, these target candidates were validated using molecular simulations (molecular docking, absorption, distribution, metabolism, excretion, and toxicity [ADMET] prediction, and molecular dynamics simulations) and biological experiments (Western blotting and fluorescence staining) in vitro and in vivo .
RESULTS:AI-driven drug screening techniques have the potential to greatly accelerate drug development. A compound called HG9-91-01, identified using AI methods, exerted neuroprotective effects in acute glaucoma. Our research indicates that all five candidates recommended by AI were able to protect the morphological integrity of RGC cells when exposed to hypoxia and glucose deficiency, and HG9-91-01 showed a higher cell survival rate compared to the other candidates. Furthermore, HG9-91-01 was found to protect the retinal structure and reduce the loss of retinal layers in an acute glaucoma model. It was also observed that the neuroprotective effects of HG9-91-01 were highly correlated with the inhibition of PANoptosis (apoptosis, pyroptosis, and necroptosis). Finally, we found that HG9-91-01 can regulate key proteins related to PANoptosis, indicating that this compound exerts neuroprotective effects in the retina by inhibiting the expression of proteins related to apoptosis, pyroptosis, and necroptosis.
CONCLUSION:AI-enabled drug discovery revealed that HG9-91-01 could serve as a potential treatment for acute glaucoma.
