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Information Journal Paper

Title

Molecular Docking and Fragment-Based QSAR Modeling for In Silico Screening of Approved Drugs and Candidate Compounds Against COVID-19

Pages

  83-88

Abstract

 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 (M pro ) 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 M pro 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 M pro 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-M pro interactions. Furthermore, the top 40 drugs were selected based on screening the results for possible drug-M pro 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 M pro and ACE2. However, prior to conducting clinical trials, further experimental validation is needed.

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    Cite

    APA: Copy

    AFSHAR, SAEID, Bahmani, Asrin, & SAIDIJAM, MASSOUD. (2020). Molecular Docking and Fragment-Based QSAR Modeling for In Silico Screening of Approved Drugs and Candidate Compounds Against COVID-19. AVICENNA JOURNAL OF MEDICAL BIOCHEMISTRY, 8(2), 83-88. SID. https://sid.ir/paper/693284/en

    Vancouver: Copy

    AFSHAR SAEID, Bahmani Asrin, SAIDIJAM MASSOUD. Molecular Docking and Fragment-Based QSAR Modeling for In Silico Screening of Approved Drugs and Candidate Compounds Against COVID-19. AVICENNA JOURNAL OF MEDICAL BIOCHEMISTRY[Internet]. 2020;8(2):83-88. Available from: https://sid.ir/paper/693284/en

    IEEE: Copy

    SAEID AFSHAR, Asrin Bahmani, and MASSOUD SAIDIJAM, “Molecular Docking and Fragment-Based QSAR Modeling for In Silico Screening of Approved Drugs and Candidate Compounds Against COVID-19,” AVICENNA JOURNAL OF MEDICAL BIOCHEMISTRY, vol. 8, no. 2, pp. 83–88, 2020, [Online]. Available: https://sid.ir/paper/693284/en

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