{"title":"Discovery of novel quinoline papain-like protease inhibitors for COVID-19 through topology constrained molecular generative model","authors":"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","doi":"10.1101/2024.09.07.611841","DOIUrl":null,"url":null,"abstract":"Papain-like protease (PL<sup>pro</sup>) 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 PL<sup>pro</sup> 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 PL<sup>pro</sup> 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 PL<sup>pro</sup> inhibitory potency and favorable pharmacokinetic properties, which warrants further development as a potential candidate compound for COVID-19 disease.","PeriodicalId":501357,"journal":{"name":"bioRxiv - Microbiology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"bioRxiv - Microbiology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2024.09.07.611841","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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.