Towards Developing Quantitative Structure-activity Relationship Models for the Design of Novel Influenza A Inhibitors Targeting Neuraminidase

Ly Cong Thanh
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Abstract

Abstract: Anti-influenza-A drugs targeting viral neuraminidase have been in use for two decades. In this study, the quantitative structure-activity relationships (QSAR) of oseltamivir derivatives as influenza neuraminidase (IN) inhibitors have been explored using the Monte Carlo method based on the target function involving the index of the ideality of correlation. Three best-obtained models showed appropriate performance with R2 values of training and test sets ranging from 0.71 to 0.86, respectively. Based on the structural information extracted from the models, new inhibitors were designed and predicted for IN activities. Finally, protein docking was applied to confirm their target binding ability. Keyword: Anti-influenza A; neuramidase inhibitor; drug design; QSAR; docking.    
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为设计新型靶向神经氨酸酶的甲型流感抑制剂建立定量构效关系模型
摘要:针对病毒神经氨酸酶的抗流感药物已经使用了20年。本研究采用蒙特卡罗方法,基于相关理想性指标的目标函数,探讨了奥司他韦衍生物作为流感神经氨酸酶抑制剂的定量构效关系(QSAR)。3个最佳模型的训练集和测试集的R2值分别在0.71 ~ 0.86之间,表现出较好的性能。基于从模型中提取的结构信息,设计并预测了新的抑制剂的IN活性。最后,通过蛋白对接来确认它们的靶标结合能力。关键词:抗甲型流感;neuramidase抑制剂;药物设计;构象;对接。
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