A Novel de novo Design Study of Potent SARS-CoV-2 Main Protease Inhibitors Based on Reinforcement Learning and Molecular Docking
Hanyang Qu, Shengpeng Wang, Mingyang He, Yuhui Wu, Fei Yan, Tiaotiao Liu, Meiling Zhang · Research Square · 2023
Abstract The outbreak of coronavirus disease 2019 (COVID-19) SARS-CoV-2 has caused widespread panic in the world and has mutated at an extremely rapid rate and thus there is an urgent need for the development of COVID-19 inhibitors. In this study, we used a de novo design method, which integrates a recurrent neural network, reinforcement learning and molecular docking to generate inhibitors of SARS-CoV-2 main protease. Approximately 30,000 molecules were generated after a 120h generation process, and multiple physicochemical filters and molecular docking scores were used for further screening. Finally, five molecules were selected as drug candidates, and their binding stability was verified by molecular dynamics simulation and binding free energy analysis. The results showed that these molecules could be used as candidates for further generation and testing against SARS-CoV-2. Besides, a pharmacophore model based on superior molecules was constructed to provide a reference for subsequent drug screening.