Computational chemistry has always played a key role in anti-viral drug development. The challenges and the quickly rising public interest when a virus is becoming a threat has significantly influenced computational drug discovery. The most obvious example is anti-AIDS research, where HIV protease and reverse transcriptase have triggered enormous efforts in developing and improving computational methods. Methods applied to anti-viral research include (i) ligandbased approaches that rely on known active compounds to extrapolate biological activity, such as machine learning techniques or classical QSAR, (ii) structure-based methods that rely on an experimentally determined 3D structure of the targets, such as molecular docking or molecular dynamics, and (iii) universal approaches that can be applied in a structure- or ligand-based way, such as 3D QSAR or 3D pharmacophore elucidation. In this review we summarize these molecular modeling approaches as they were applied to fight anti-viral diseases and highlight their importance for anti-viral research. We discuss the role of computational chemistry in the development of small molecules as agents against HIV integrase, HIV-1 protease, HIV-1 reverse transcriptase, the influenza virus M2 channel protein, influenza virus neuraminidase, the SARS coronavirus main proteinase and spike protein, thymidine kinases of herpes viruses, hepatitis C virus proteins and other flaviviruses as well as human rhinovirus coat protein and proteases, and other picornaviridae. We highlight how computational approaches have helped in discovering anti-viral activities of natural products and give an overview on polypharmacology approaches that help to optimize drugs against several viruses or help to optimize the metabolic profile of and anti-viral drug.
Keywords: Activity profiling, computational chemistry, docking, drugs from natural sources, fingerprints, HCV NS3/4A serine protease, HCV NS5B RNA-dependent RNA-polymerase, hepatitis C virus, herpes, HIV, HIV integrase, HIV-1 protease, HIV-1 reverse transcriptase, homology modeling, HRV capsid protein, HRV protease 2A, HRV protease 3C, HSV, human -glucosidase, influenza virus, inverse screening, lead structure development, M2 channel protein, molecular dynamics simula-tion, molecular interaction fields, molecular modeling, natural compounds, neuraminidase, parallel screening, pharmacophore modeling, QSAR, SARS-CoV, similarity-based screening, thymidine kinase, viral disease, virtual screening, virus
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