Development of 3D-QSAR combination approach for discovering and analysing neuraminidase inhibitors in silico Online publication date: Tue, 21-Oct-2014
by Chun-Yuan Lin; Hsiao-Chieh Chi; Kuei-Chung Shih; Jiayi Zhou; Nai-Wan Hsiao; Chuan-Yi Tang
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 9, No. 3, 2014
Abstract: Zanamivir and Oseltamivir are both sialic acid analog inhibitors of Neuraminidase (NA), which is an important target in influenza A virus treatment. Quantitative Structure-Activity Relationships (QSAR) is a common computational method for correlating the structural properties of compounds (or inhibitors) with their biological activities. The pharmcophore model easily and quickly recognises related inhibitors and also fits the binding site interaction features of a protein structure. The Comparative Molecular Similarity Index Analysis (CoMSIA) model easily optimises molecular structures and describes the limit range of molecule weights. This study proposes a combination approach that integrates these two models based on the same training set inhibitors in order to screen and optimize NA inhibitor candidates during drug design.
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