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.
Keywords: Docking, hemagglutinin, influenza virus, M2 proton channel, neuraminidase, pharmacophore, RNA polymerase.
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