Computer-aided drug design for virtual-screening and active-predicting of main protease (M(pro)) inhibitors against SARS-CoV-2

计算机辅助药物设计用于虚拟筛选和主动预测针对 SARS-CoV-2 的主要蛋白酶 (M(pro)) 抑制剂

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Abstract

Introduction: SARS-CoV-2 is a novel coronavirus with highly contagious and has posed a significant threat to global public health. The main protease (M(pro)) is a promising target for antiviral drugs against SARS-CoV-2. Methods: In this study, we have used pharmacophore-based drug design technology to identify potential compounds from drug databases as M(pro) inhibitors. Results: The procedure involves pharmacophore modeling, validation, and pharmacophore-based virtual screening, which identifies 257 compounds with promising inhibitory activity. Discussion: Molecular docking and non-bonding interactions between the targeted protein M(pro) and compounds showed that ENA482732 was the best compound. These results provided a theoretical foundation for future studies of M(pro) inhibitors against SARS-CoV-2.

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