Recent Advances in Computer-Aided Drug Design as Applied to Anti-Influenza Drug Discovery

Abstract

Influenza is a seasonal and serious health threat, and the recent outbreak of H7N9 following the pandemic spread of H1N1 in 2009 has served to emphasize the importance of anti-influenza drug discovery. Zanamivir (Relenza™) and oseltamivir (Tamiflu(®)) are two antiviral drugs currently recommended by the CDC for treating influenza. Both are examples of the successful application of structure-based drug design strategies. These strategies have combined computer- based approaches, such as docking- and pharmacophore-based virtual screening with X-ray crystallographic structural analyses. Docking is a routinely used computational method to identify potential hits from large compound libraries. This method has evolved from simple rigid docking approaches to flexible docking methods to handle receptor flexibility and to enhance hit rates in virtual screening. Virtual screening approaches can employ both ligand-based and structurebased pharmacophore models depending on the available information. The exponential growth in computing power has increasingly facilitated the application of computer-aided methods in drug discovery, and they now play significant roles in the search for novel therapeutics. An overview of these computational tools is presented in this review, and recent advances and challenges will be discussed. The focus of the review will be anti-influenza drug discovery and how advances in our understanding of viral biology have led to the discovery of novel influenza protein targets. Also discussed will be strategies to circumvent the problem of resistance emerging from rapid mutations that has seriously compromised the efficacy of current anti-influenza therapies.