The COVID-19 pandemic’s profound impact on global health and the economy underscores the need for new computational strategies to rapidly identify potential antiviral drugs against emerging and re-emerging infectious diseases. AI-based drug repurposing methods have the potential to significantly shorten the drug discovery process, rendering them a crucial and efficacious approach for screening anti-SARS-CoV-2 compounds. In this paper, we proposed GINCM-DTA, an AI-based graph isomorphism network with protein contact map representation for drug–target affinity (DTA) prediction, and presented the COVID-DTA dataset for SARS-CoV-2-specific drug repurposing. Pre-training GINCM-DTA on large-scale Davis and KIBA datasets achieved state-of-the-art performance, while fine-tuning on the COVID-DTA dataset using transfer learning resulted in a mean square error of 0.026 and concordance index of 0.967. Utilizing the fine-tuned GINCM-DTA model, we predicted DTAs for drugs against SARS-CoV-2 main targets, focusing on key conserved proteins and unmutated spike proteins for virtual screening. We integrated GINCM-DTA predictions with molecular docking simulations to identify five candidates. In vitro assays revealed Hydralazine (5.737 nM) as a potential inhibitor of ACE2 and TMPRSS2, effectively blocking SARS-CoV-2 infection. Moreover, the results highlight the potential of Hydralazine as a broad-spectrum drug candidate, demonstrating its efficacy in obstructing the binding between ACE2 and spike proteins of multiple Omicron variants, including the currently dominant strains BQ.1, BQ.1.1, XBB.1.5, and XBB.1.16. Our method offers a novel computational strategy for COVID-19 antiviral screening and prepares for potential future epidemics incited by emerging viruses.
Currently, human health due to corona virus disease 2019 (COVID-19) pandemic has been seriously threatened. The coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (S) protein plays a crucial role in virus transmission and several S-based therapeutic approaches have been approved for the treatment of COVID-19. However, the efficacy is compromised by the SARS-CoV-2 evolvement and mutation. Here we report the SARS-CoV-2 S protein receptor-binding domain (RBD) inhibitor licorice-saponin A3 (A3) could widely inhibit RBD of SARS-CoV-2 variants, including Beta, Delta, and Omicron BA.1, XBB and BQ1.1. Furthermore, A3 could potently inhibit SARS-CoV-2 Omicron virus in Vero E6 cells, with EC 50 of 1.016 μM. The mechanism was related with binding with Y453 of RBD determined by hydrogen-deuterium exchange mass spectrometry (HDX-MS) analysis combined with quantum mechanics/molecular mechanics (QM/MM) simulations. Interestingly, phosphoproteomics analysis and multi fluorescent immunohistochemistry (mIHC) respectively indicated that A3 also inhibits host inflammation by directly modulating the JNK and p38 MAPK pathways and rebalancing the corresponding immune dysregulation. This work supports A3 as a promising broad-spectrum small molecule drug candidate for COVID-19.
危险品化学品经营许可证（带存储）