Artificial intelligence-driven discovery of novel scaffolds for selective TLR7 antagonists and their application in enhancing mRNA translation efficiency

Abstract

Toll-like receptor 7 (TLR7) is crucial in the innate immune response, responsible for recognizing single-stranded RNA from external pathogens and initiating the production of inflammatory cytokines and type I interferons. Despite the potential therapeutic benefits of modulating TLR7 activity, particularly in autoimmune diseases and viral infections, the development of TLR7 antagonists remains limited compared to that of TLR7 agonists. Therefore, this study aims to utilize artificial intelligence to identify novel scaffolds for TLR7 antagonists. Using MotifGen, thousands of potential TLR7-binding compounds were screened, followed by ligand-docking simulations to narrow down the selection to 50 candidates. Of these, 10 compounds with high docking scores for TLR7 and distinct structures were selected. Among them, two promising TLR7 antagonists were identified: 8-Methoxy-N-[(2,4,5,6-tetrahydro-2-methyl-3-cyclopentapyrazol)methyl]-5-quinoline and N-ethyl-2-[(5-fluoro-2,6-dimethyl-4-pyrimidinyl)amino]-N-(phenylmethyl)acetamide. Both compounds exhibited low IC50 values, high selectivity for TLR7 over TLR8 and TLR9, and low cytotoxicity. Additionally, these antagonists showed potential for enhancing mRNA translation efficiency, suggesting their utility in mRNA-based therapeutics. These findings highlight the potential of these novel TLR7 antagonists in treating autoimmune diseases and advancing mRNA therapeutic applications.