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.

尽管新冠疫情在全球范围内已得到一定程度的控制，但业界对于利用人工智能（AI）研发新冠小分子抑制剂的可行性仍存在争议。本研究探究了利用AI设计新冠病毒有效抑制剂的可行性，通过将生成式模型与强化学习、分子对接技术相结合，设计了靶向新冠病毒3CL蛋白酶的小分子抑制剂。经分子对接评分与理化性质筛选后，共获得5种候选抑制剂。此外，理论计算证实，这些候选抑制剂与新冠病毒3CL蛋白酶具有显著的结合稳定性，其效果可媲美甚至优于现有新冠病毒抑制剂。同时，通过基于配体的药效团模型筛选，本研究验证了生成式模型的有效性，展现了AI在药物设计领域的潜在应用价值。