通过拓扑约束分子生成模型发现新型喹啉木瓜蛋白酶样蛋白酶 COVID-19 抑制剂

Jinsai Shang, Ting Ran, Yongzhi Lu, Qi Yang, Guihua Zhang, Peiqi Zhou, Wenqi Li, Minyuan Xu, Jielin Tang, Minxian Dai, Jinpeng Zhong, Hua Chen, Pan He, Anqi Zhou, Bao Xue, Jiayi Chen, Jiyun Zhang, Kunzhong Wu, Xinyu Wu, Miru Tang, Xinwen Chen, Hongming Chen
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摘要

在导致 COVID-19 的 SARS-CoV-2 感染中,木瓜蛋白酶(Papain-like protease,PLpro)在病毒多聚蛋白加工和宿主抗病毒免疫抑制中发挥着关键作用。虽然有几种治疗 COVID-19 的药物已被批准,如 Remdesivir、Nirmatrelvir 等,但还没有一种 PLpro 抑制剂被批准用于治疗 COVID-19。基于人工智能的药物设计方法的出现大大加快了药物发现的进程。在本研究中,通过利用拓扑约束分子生成模型的力量,我们发现了一系列新型 PLpro 抑制剂,它们对流行的 SARS-CoV-2 变体有很强的抑制作用。通过基于结构的计算方法进行优化,我们的先导化合物 GZNL-2002 获得了很高的 PLpro 抑制效力和良好的药代动力学特性,值得作为 COVID-19 疾病的潜在候选化合物进一步开发。
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Discovery of novel quinoline papain-like protease inhibitors for COVID-19 through topology constrained molecular generative model
Papain-like protease (PLpro) plays a critical role in both viral polyprotein processing and host antiviral immune suppression in SARS-CoV-2 infection, which causes COVID-19. Although several drugs have been approved for COVID-19, such as Remdesivir, Nirmatrelvir, etc., none of the PLpro inhibitors have been approved for the treatment of COVID-19. The advent of artificial intelligence-based drug design methods has significantly accelerated the process of drug discovery. In current study, by harnessing the power of a topology constrained molecular generative model, we discovered a novel series of PLpro inhibitors with strong potency against prevalent SARS-CoV-2 variants. Following a structure based computational approach for optimization, our lead compound, GZNL-2002, achieved decent PLpro inhibitory potency and favorable pharmacokinetic properties, which warrants further development as a potential candidate compound for COVID-19 disease.
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