Background: COVID-19 is a pandemic respiratory contagious viral (SARS-CoV-2) disease associated with high morbidity and mortality worldwide. Currently, there are no effective preventive or treatment strategies for COVID-19 and it has been declared as a global health emergency by WHO. In silico molecular docking studies can be useful to predict the binding affinity between the phytocompound and the target protein and play a vital role in finding an inhibitor through structure-based drug design.

Objective: In this aspect, our objective was to screen essential flavonoids against possible protein targets such as SARS-CoV-2 spike glycoprotein receptor binding domain (RBD-S) and host Angiotensin Converting Enzyme-2 protease domain (PD-ACE-2) using in silico molecular docking studies.

Methods: Approximately 49 flavonoids were identified and were evaluated for their drug-likeness based on Lipinski rule, bioactivity scores, antiviral and toxicity profiles using SwissADME, Molinspiration, PASS and GUSAR online tools. The flavonoids that passed Lipinski rule were subjected to in silico analysis through molecular docking on RBD-S and PD-ACE-2 using Molegro Virtual Docker v6.0.

Results: The bioactive flavonoids that showed NIL violations and were found in compliance with Lipinski rule were selected for docking studies. In silico analysis reported that biochanin A and silymarin bind significantly at the active sites of RBD-S and PD-ACE-2 with a MolDock score of -78.41and -121.28 kcal/mol respectively. Bioactivity scores, antiviral potential and toxicity profiles were predicted for the top interacting phytocompounds and substantial relevant data was reported.

Conclusion: The current outcomes created a new paradigm for understanding biochanin A and silymarin bioflavonoids as potent inhibitors of RBD-S and PD-ACE-2 targets respectively. Further work can be extended to confirm their therapeutic potential for COVID-19.

背景：COVID-19 是一种大流行性呼吸道传染性病毒 (SARS-CoV-2) 疾病，在全球范围内与高发病率和死亡率相关。目前，没有针对 COVID-19 的有效预防或治疗策略，并已被世卫组织宣布为全球卫生紧急情况。计算机分子对接研究可用于预测植物化合物与目标蛋白之间的结合亲和力，并在通过基于结构的药物设计寻找抑制剂方面发挥重要作用。

目的：在这方面，我们的目标是针对可能的蛋白质靶标筛选必需黄酮类化合物，例如 SARS-CoV-2 刺突糖蛋白受体结合域 (RBD-S) 和宿主血管紧张素转换酶-2 蛋白酶域 (PD-ACE-2)使用 in silico 分子对接研究。

方法：使用 SwissADME、Molinspiration、PASS 和 GUSAR 在线工具，根据 Lipinski 规则、生物活性评分、抗病毒和毒性特征，鉴定了大约 49 种黄酮类化合物并评估了它们的药物相似性。使用 Molegro Virtual Docker v6.0，通过在 RBD-S 和 PD-ACE-2 上进行分子对接，对通过 Lipinski 规则的类黄酮进行计算机分析。

结果：选择显示 NIL 违规且符合 Lipinski 规则的生物活性黄酮类化合物进行对接研究。计算机分析报告称，biochanin A 和水飞蓟素与 RBD-S 和 PD-ACE-2 的活性位点显着结合，MolDock 评分分别为 -78.41 和 -121.28 kcal/mol。预测了相互作用最大的植物化合物的生物活性评分、抗病毒潜力和毒性特征，并报告了大量相关数据。

结论：目前的结果为理解 biochanin A 和水飞蓟素生物类黄酮分别作为 RBD-S 和 PD-ACE-2 靶标的有效抑制剂创造了新的范式。可以扩展进一步的工作以确认它们对 COVID-19 的治疗潜力。