Discovery of novel quinoline papain-like protease inhibitors for COVID-19 through topology constrained molecular generative model
Shang, J.; Ran, T.; Lu, Y.; Yang, Q.; Zhang, G.; Zhou, P.; Li, W.; Xu, M.; Dai, M.; Zhong, J.; Chen, H.; He, P.; Zhou, A.; Xue, B.; Chen, J.; Zhang, J.; Wu, K.; Wu, X.; Tang, M.; Chen, X.; Chen, H.
Show 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.
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