Is it feasible to use AI-based drug design methods in the process of generating effective COVID-19 inhibitors? A validation study using molecular docking, molecular simulation, and pharmacophore methods

Although the COVID-19 pandemic has been brought under control to some extent globally, there is still debate in the industry about the feasibility of using artificial intelligence (AI) to generate COVID small-molecule inhibitors. In this study, we explored the feasibility of using AI to design effective inhibitors of COVID-19. By combining a generative model with reinforcement learning and molecular docking, we designed small-molecule inhibitors targeting the COVID-19 3CLpro enzyme. After screening based on molecular docking scores and physicochemical properties, we obtained five candidate inhibitors. Furthermore, theoretical calculations confirmed that these candidate inhibitors have significant binding stability with COVID-19 3CLpro, comparable to or better than existing COVID-19 inhibitors. Additionally, through ligand-based pharmacophore model screening, we validated the effectiveness of the generative model, demonstrating the potential value of AI in drug design.