Introduction: The emergence of the highly pathogenic avian influenza (HPAI) H5N1 virus and the recent global circulation of H1N1 swine-origin influenza virus in 2009 have highlighted the need for new anti-influenza therapies. This has been made all the more important with the emergence of antiviral-resistant strains. Recent progress in achieving three-dimensional (3D) crystal structures of influenza viral proteins and efficient tools available for pharmacophore-based virtual screening are aiding us in the discovery and design of new antiviral compounds. Areas covered: This review discusses pharmacophore modeling as a potential cost-effective and time-saving technology for new drug discovery as an alternative to high-throughput screening. Based on this technical platform, the authors discuss current progress and future prospects for developing novel influenza antivirals against pre-existing or emerging novel targets. Expert opinion: Although it might be at an infant stage of development, the availability of the 3D crystal structures of influenza viral proteins is expected to accelerate the application of structure-based drug design (SBDD) and pharmacophore modeling. Furthermore, the neuraminidase inhibitor, one of the most successful examples of a SBDD, still receives great attention because of its superb antiviral activities and the resistance of influenza strains to oseltamivir. However, despite much success, pharmacophore-based virtual screening exhibits limited predictive power in hit identification. Further improvements in pharmacophore detection algorithms, proper combinations of in silico methods as well as judicious choosing of compounds are expected to improve the hit rate. With the help of these technologies, the discovery of anti-influenza agents will be accelerated.
Bibliographical noteFunding Information:
The authors declare that this study was supported in part by a grant from the Korea Health Technology R&D project and the Ministry of Health & Welfare of the Republic of Korea (A085105), the Ministry of Education, Science, and Technology (grant number 2012-0000887) of Korean government and the R&D Program of Ministry of Knowledge Economy/Korea Evaluation Institute of Industrial Technology (MKE/KEIT) (10031969). Also, the authors like to thank Dr. Ji-Young Lee from Bioinformatics & Molecular Design Center (Seoul, Korea) for Figure 4.
All Science Journal Classification (ASJC) codes
- Drug Discovery