Hamadan University of Medical Sciences Avicenna Journal of Medical Biochemistry 2345-4113 8 2 2020 12 30 Molecular Docking and Fragment-Based QSAR Modeling for In Silico Screening of Approved Drugs and Candidate Compounds Against COVID-19 83 88 10.34172/ajmb.2020.12 EN Saeid Afshar https://orcid.org/0000-0001-5136-4259 Asrin Bahmani Massoud Saidijam https://orcid.org/0000-0001-8910-556X Journal Article 10.34172/ajmb.2020.12 2020 11 16 2020 11 30 Background: Coronavirus disease 2019 (COVID-19) as a serious global health crisis leads to high mortality and morbidity. However, currently, there are no effective vaccines and treatments for COVID-19. Main protease (Mpro) and angiotensin-converting enzyme 2 (ACE2) are the best therapeutic targets of COVID-19. Objectives: The main purpose of this study is to investigate the most appropriate drug and candidate compound for proper interaction with Mpro and ACE2 to inhibit the activity of COVID-19. Methods: In this study, repurposing of approved drugs and screening of candidate compounds using molecular docking and fragment-based QSAR method were performed to discover the potential inhibitors of Mpro and ACE2. QSAR and docking calculations were performed based on the prediction of the inhibitory activities of 5-hydroxy indanone derivatives. Based on the results, an optimal structure was proposed to inhibit the activity of COVID-19. Results: Among 2629 DrugBank approved drugs, 118 were selected considering the LibDock score and absolute energy for possible drug-Mpro interactions. Furthermore, the top 40 drugs were selected based on screening the results for possible drug- Mpro interactions with AutoDock Vina. Conclusion: Finally, evaluation of the top 40 selected drugs for possible drug-ACE2 interactions with AutoDock Vina indicated that deslanoside (DB01078) can interact effectively with both Mpro and ACE2. However, prior to conducting clinical trials, further experimental validation is needed.