QSAR and molecular mechanism analysis of N-substituted oseltamivir derivatives as potent avian influenza H5N1 neuraminidase inhibitors

Sun, Jiaying; Mei, Hu

doi:10.1016/j.chemolab.2015.07.005

Cited by 8 publications

(1 citation statement)

References 35 publications

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“…Xue et al [ 17 ] used heuristic algorithm (HM) and support vector machine (SVM) to construct prediction models to predict the binding affinity of 94 compounds to human serum albumin, leading to a good correlation coefficient (r 2 ) of 0.86 and 0.94 and root-mean-square errors of 0.212 and 0.134 albumin drug binding affinity units, respectively. Sun et al [ 18 ] constructed four QSAR models (PLS, HQSAR, CoMSIA and Almond model) by using 32 N-substituted oseltamivir derivatives NAIs. The r 2 and q 2 of the optimal model are 0.950 and 0.846, respectively.…”

Section: Introductionmentioning

confidence: 99%

2D-SAR, Topomer CoMFA and molecular docking studies on avian influenza neuraminidase inhibitors

Niu

et al. 2019

Computational and Structural Biotechnology Journal

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Avian influenza is a serious zoonotic infectious disease with huge negative impacts on local poultry farming, human health and social stability. Therefore, the design of new compounds against avian influenza has been the focus in this field. In this study, computational methods were applied to investigate the compounds with neuraminidase inhibitory activity. First, 2D-SAR model was built to recognize neuraminidase inhibitors (NAIs). As a result, the accuracy of 10 cross-validation and independent tests is 96.84% and 98.97%, respectively. Then, the Topomer CoMFA model was constructed to predict the inhibitory activity and analyses molecular fields. Two models were obtained by changing the cutting methods. The second model is employed to predict the activity (q2 = 0.784 and r2 = 0.982). Molecular docking was also used to further analyze the binding sites between NAIs and neuraminidase from human and avian virus. As a result, it is found that same binding Total Score has some differences, but the binding sites are basically the same. At last, some potential NAIs were screened and some optimal opinions were taken. It is expected that our study can assist to study and develop new types of NAIs.

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Section: Introductionmentioning

confidence: 99%