Molecular Docking and Virtual Screening Based Prediction of Drugs for COVID-19

Aims: To predict potential drugs for COVID-19 by using molecular docking for virtual screening of drugs approved for other clinical applications. Background: SARS-CoV-2 is the betacoronavirus responsible for the COVID-19 pandemic. It was listed as a potential global health threat by the WHO due to high mortality, high basic reproduction number, and lack of clinically approved drugs and vaccines. The genome of the virus responsible for COVID-19 has been sequenced. In addition, the three-dimensional structure of the main protease has been determined experimentally. Objective: To identify potential drugs that can be repurposed for treatment of COVID-19 by using molecular docking based virtual screening of all approved drugs. Methods: A list of drugs approved for clinical use was obtained from the SuperDRUG2 database. The structure of the target in the apo form, as well as structures of several target-ligand complexes, were obtained from RCSB PDB. The structure of SARS-CoV-2 Mpro determined from X-ray diffraction data was used as the target. Data regarding drugs in clinical trials for COVID-19 was obtained from clinicaltrials.org. Input for molecular docking based virtual screening was prepared by using Obabel and customized python, bash, and awk scripts. Molecular docking calculations were carried out with Vina and SMINA, and the docked conformations were analyzed and visualized with PLIP, Pymol, and Rasmol. Results: Among the drugs that are being tested in clinical trials for COVID-19, Danoprevir and Darunavir were predicted to have the highest binding affinity for the Main protease (Mpro) target of SARS-CoV-2. Saquinavir and Beclabuvir were identified as the best novel candidates for COVID-19 therapy by using Virtual Screening of drugs approved for other clinical indications. Conclusion: Protease inhibitors approved for treatment of other viral diseases have the potential to be repurposed for treatment of COVID-19.