Tu Xing, Zou Zixing, Li Jiahui, Zeng Simiao, Luo Zhengchao, Li Gen, Gao Yuanxu, Zhang Kang
Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, China.
Guangzhou National Laboratory, Guangzhou International Bio Island, Guangzhou, Guangdong 510530, China.
Chin Med J (Engl). 2025 Jan 20;138(2):172-184. doi: 10.1097/CM9.0000000000003387. Epub 2024 Dec 23.
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.
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 .
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.
AI-enabled drug discovery revealed that HG9-91-01 could serve as a potential treatment for acute glaucoma.
急性高眼压导致的视网膜神经节细胞（RGC）死亡是急性青光眼的一个重要特征。介导坏死性凋亡的受体相互作用蛋白激酶3（RIPK3）是RGC死亡的一个潜在治疗靶点。然而，目前对RIPK3在青光眼治疗中的靶向药物和机制的了解仍然有限。值得注意的是，人工智能（AI）技术在药物发现方面取得了显著进展。本研究旨在借助AI发现RIPK3抑制剂。
采用急性高眼压模型在体内模拟病理性高眼压。我们运用了一系列AI方法，包括大语言模型和图神经网络模型，来识别RIPK3的靶标化合物。随后，这些候选靶标在体外和体内通过分子模拟（分子对接、吸收、分布、代谢、排泄和毒性[ADMET]预测以及分子动力学模拟）和生物学实验（蛋白质免疫印迹法和荧光染色）进行验证。
AI驱动的药物筛选技术有潜力极大地加速药物开发。使用AI方法鉴定出的一种名为HG9-91-01的化合物在急性青光眼中发挥了神经保护作用。我们的研究表明，AI推荐的所有五个候选物在暴露于缺氧和葡萄糖缺乏时都能够保护RGC细胞的形态完整性，并且HG9-91-01与其他候选物相比显示出更高的细胞存活率。此外，在急性青光眼模型中发现HG9-91-01可保护视网膜结构并减少视网膜层的损失。还观察到HG9-91-01的神经保护作用与对PAN凋亡（凋亡、焦亡和坏死性凋亡）的抑制高度相关。最后，我们发现HG9-91-01可以调节与PAN凋亡相关的关键蛋白，表明该化合物通过抑制与凋亡、焦亡和坏死性凋亡相关的蛋白表达在视网膜中发挥神经保护作用。
基于AI的药物发现表明HG9-91-01可作为急性青光眼的一种潜在治疗方法。
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