Discovery of resistance-resilient quinoline papain-like protease inhibitors through topology-constrained molecular generative design

The rapid emergence of drug-resistant SARS-CoV-2 variants poses a persistent challenge to current antiviral strategies. Mutations in key viral targets, including RNA-dependent RNA polymerase (RdRp), main protease (3CLpro), and papain-like protease (PLpro), have been shown to markedly reduce the efficacy of approved therapeutics, highlighting the urgent need for next-generation antivirals capable of overcoming resistance. Here, we report the discovery of a novel class of PLpro inhibitors through an AIDD strategy based on a topology-constrained molecular generative model (Tree-Invent) integrated with structure-guided optimization. Scaffold hopping from a previously reported lead enabled the identification of a quinoline-based chemical series with substantially improved metabolic stability and antiviral potency. Structure-guided optimization yielded compound GZNL-2016, which exhibited potent enzymatic inhibition of PLpro (IC50 = 10.5 nM), robust antiviral activity against multiple SARS-CoV-2 variants, including Omicron BA.5 and XBB.1, and favorable pharmacokinetic properties following oral administration. Notably, GZNL-2016 retained substantial inhibitory activity against the clinically relevant drug-resistant mutant PLpro E167K (IC50 = 480.2 nM; Ki = 439.3 nM), in contrast to previously reported inhibitors that exhibit markedly reduced potency. In a SARS-CoV-2 infection mouse model, oral administration of GZNL-2016 significantly reduced pulmonary viral titers, demonstrating in vivo antiviral efficacy. Collectively, this study establishes an AI-enabled strategy for rapid antiviral discovery and identifies GZNL-2016 as a promising lead compound to address the threat of coronavirus infections caused by drug-resistant mutant SARS-CoV-2 variants. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/611841v3_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@ecbd3eorg.highwire.dtl.DTLVardef@7c663corg.highwire.dtl.DTLVardef@11c0e37org.highwire.dtl.DTLVardef@e950f0_HPS_FORMAT_FIGEXP M_FIG C_FIG We leveraged an AI generative model to discover a novel PLpro inhibitor with excellent liver stability, low CYP, hERG inhibition and reasonable oral PK properties. At the same time, the compound 16 exhibits high efficacy for the resistance mutation E167K, which resulted in severe resistance to the previously reported inhibitor Jun12682 and PF-07957472.