Novel and Predictive QSAR Model and Molecular Docking: New Natural Sulfonamides of Potential Concern Against SARS-Cov-2

Nathalie Moussa, Hoda Mando
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Abstract

Since the outbreak of the COVID-19 pandemic in 2019, the world has been racing to develop effective drugs for treating this deadly disease. Although there are now some vaccines that have somewhat alleviated global panic, the lack of approved drugs remains a persistent challenge. Consequently, there is a pressing need to discover new therapeutic molecules. In this study, we explore the application of a quantitative structure−activity relationship (QSAR) model to predict the efficacy of 28 cyclic sulfonamide derivatives against SARS-CoV-2. The model was developed using multiple linear regression, and six molecular descriptors were identified as the most significant factors in determining the inhibitory activity. This proposed QSAR model holds the potential for aiding the virtual screening and drug design process in the development of new and more effective SARS-CoV-2 inhibitors. The model was also applied to seven natural products primary sulfonamides and sulfamates, demonstrating promising activity The study results indicated that the atom count, as represented by the descriptor nCl, had the most significant impact on the inhibitory activity against SARS-CoV-2. The proposed model was validated using various statistical parameters, confirming its validity, robustness, and predictiveness, with a high correlation coefficient (R2) of 0.77 for the training group and 0.95 for the test group. Furthermore, we predicted the activity of seven natural compounds, and among them, Dealanylascamycin exhibited the highest predicted activity. Subsequently, Dealanylascamycin was docked to SARS-CoV-2 and the results of the docking study further strengthened its potential as a promising candidate against COVID-19, suggesting that it should be considered for further optimization and validation. Our findings demonstrate promising predicted inhibitory activity against SARS-CoV-2 for seven natural products, primary sulfonamides, and primary sulfamates.
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新型预测QSAR模型与分子对接:抗SARS-Cov-2潜在关注的新型天然磺胺类药物
自2019年COVID-19大流行爆发以来,世界一直在竞相开发治疗这种致命疾病的有效药物。虽然现在有一些疫苗在一定程度上缓解了全球恐慌,但缺乏批准的药物仍然是一个持续的挑战。因此,迫切需要发现新的治疗分子。在这项研究中,我们探索了定量结构-活性关系(QSAR)模型的应用,以预测28种环磺胺衍生物对SARS-CoV-2的疗效。利用多元线性回归建立模型,确定了6个分子描述符是决定抑制活性的最重要因素。该提出的QSAR模型具有帮助开发新的和更有效的SARS-CoV-2抑制剂的虚拟筛选和药物设计过程的潜力。该模型还应用于7种天然产物磺胺类和磺胺类,显示出良好的活性。研究结果表明,以描述符nCl为代表的原子计数对抑制SARS-CoV-2的活性影响最显著。采用各种统计参数对模型进行验证,证实了模型的有效性、稳健性和预测性,训练组和试验组的相关系数(R2)分别为0.77和0.95。此外,我们预测了7种天然化合物的活性,其中Dealanylascamycin的预测活性最高。随后,我们将Dealanylascamycin与SARS-CoV-2对接,对接研究结果进一步强化了Dealanylascamycin作为抗COVID-19候选药物的潜力,值得进一步优化和验证。我们的研究结果表明,七种天然产物、原磺胺类和原磺胺类化合物对SARS-CoV-2具有良好的预测抑制活性。
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来源期刊
Anti-Infective Agents
Anti-Infective Agents Pharmacology, Toxicology and Pharmaceutics-Pharmacology
CiteScore
1.50
自引率
0.00%
发文量
47
期刊介绍: Anti-Infective Agents publishes original research articles, full-length/mini reviews, drug clinical trial studies and guest edited issues on all the latest and outstanding developments on the medicinal chemistry, biology, pharmacology and use of anti-infective and anti-parasitic agents. The scope of the journal covers all pre-clinical and clinical research on antimicrobials, antibacterials, antiviral, antifungal, and antiparasitic agents. Anti-Infective Agents is an essential journal for all infectious disease researchers in industry, academia and the health services.
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