Develop integration modeling approach for discovery neuraminidase inhibitors in silico based on pharmacophore and CoMSIA models

Neuraminidase (NA) is an important target for influenza A virus treatment, such as Zanamivir and oseltamivir both are sialic acid analog inhibitors of NA. Quantitative Structure-Activity Relationships is a widely adapted computational method that correlates the structural properties of compounds with their biological activities. The pharmcophore model can be easily and quickly used to recognize the related inhibitors, and fit the binding site interaction features of protein structure. The Comparative Molecular Similarity Index Analysis model is easily used to modify the molecule structure optimization, and describe the limit range of molecule weights. In this study, we propose a combination application approach to integrate these two models based on the same training set inhibitors to identify NA inhibitor candidates. Hence, discovering novel NA inhibitors can be screened and optimized by using our drug design approach.