Background: The novel coronavirus SARS-CoV-2 has severely affected the health and economy of several countries. Multiple studies are in progress to design novel therapeutics against the potential target proteins in SARS-CoV-2, including 3CL protease, an essential protein for virus replication. Materials & methods: In this study we employed deep neural network-based generative and predictive models for de novo design of small molecules capable of inhibiting the 3CL protease. The generative model was optimized using transfer learning and reinforcement learning to focus around the chemical space corresponding to the protease inhibitors. Multiple physicochemical property filters and virtual screening score were used for the final screening. Conclusion: We have identified 33 potential compounds as ideal candidates for further synthesis and testing against SARS-CoV-2.
【저자키워드】 COVID-19, SARS-CoV-2, deep learning, artificial intelligence, 3CL Protease, protease inhibitors, 【초록키워드】 Economy, Virtual screening, protease, protease inhibitors, Novel coronavirus, Protein, Health, Predictive model, virus replication, Small molecules, small molecule, 3CL, reinforcement learning, focus, Compound, novel coronavirus SARS-CoV-2, candidate, transfer, Final, material, Multiple studies, reinforcement, target protein, Multiple, de novo, affected, were used, inhibiting, physicochemical,