Computational Approaches to Discover Novel Natural Compounds for SARS‐CoV‐2 Therapeutics

• DOI: 10.1002/open.202000332
• Publication Date: 2021-05-19T18:20:03Z
• Authors: Shailima Rampogu; Gihwan Lee; Apoorva M. Kulkarni; Donghwan Kim; Sanghwa Yoon; Myeong Ok Kim; Keun Woo Lee
• Keywords: 2019-20 coronavirus outbreak

Scientists all over the world are facing a challenging task of finding effective therapeutics for coronavirus disease (COVID-19). One fastest ways putative drug candidates is use computational discovery approaches. The purpose current study to retrieve natural compounds that have obeyed drug-like properties as potential inhibitors. Computational molecular modelling techniques were employed discover with SARS-CoV-2 inhibition properties. Accordingly, InterBioScreen (IBS) database was obtained and prepared by minimizing compounds. To resultant compounds, absorption, distribution, metabolism, excretion toxicity (ADMET) Lipinski's Rule Five applied yield subjected dynamics simulation studies evaluate their stabilities. In article, we docking based virtual screening method using compound yielding two has hits. These demonstrated higher binding affinity scores than reference together good pharmacokinetic Additionally, identified hits displayed stable interaction results inferred results. Taken together, advocate STOCK1N-71493 STOCK1N-45683 treatment regime.