Development of anti-viral agents using molecular modeling and virtual screening techniques.
Identifieur interne : 001509 ( PubMed/Checkpoint ); précédent : 001508; suivant : 001510Development of anti-viral agents using molecular modeling and virtual screening techniques.
Auteurs : Johannes Kirchmair [Autriche] ; Simona Distinto ; Klaus Roman Liedl ; Patrick Markt ; Judith Maria Rollinger ; Daniela Schuster ; Gudrun Maria Spitzer ; Gerhard WolberSource :
- Infectious disorders drug targets [ 2212-3989 ] ; 2011.
Descripteurs français
- KwdFr :
- Antiviraux (), Antiviraux (métabolisme), Antiviraux (pharmacologie), Découverte de médicament, Humains, Modèles moléculaires, Produits biologiques (), Produits biologiques (métabolisme), Produits biologiques (pharmacologie), Relation quantitative structure-activité, Simulation de dynamique moléculaire, Simulation numérique, Tests de criblage à haut débit, Thérapie moléculaire ciblée.
- MESH :
- métabolisme : Antiviraux, Produits biologiques.
- pharmacologie : Antiviraux, Produits biologiques.
- Antiviraux, Découverte de médicament, Humains, Modèles moléculaires, Produits biologiques, Relation quantitative structure-activité, Simulation de dynamique moléculaire, Simulation numérique, Tests de criblage à haut débit, Thérapie moléculaire ciblée.
English descriptors
- KwdEn :
- Antiviral Agents (chemistry), Antiviral Agents (metabolism), Antiviral Agents (pharmacology), Biological Products (chemistry), Biological Products (metabolism), Biological Products (pharmacology), Computer Simulation, Drug Discovery, High-Throughput Screening Assays, Humans, Models, Molecular, Molecular Dynamics Simulation, Molecular Targeted Therapy, Quantitative Structure-Activity Relationship.
- MESH :
- chemical , chemistry : Antiviral Agents, Biological Products.
- chemical , metabolism : Antiviral Agents, Biological Products.
- chemical , pharmacology : Antiviral Agents, Biological Products.
- Computer Simulation, Drug Discovery, High-Throughput Screening Assays, Humans, Models, Molecular, Molecular Dynamics Simulation, Molecular Targeted Therapy, Quantitative Structure-Activity Relationship.
Abstract
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) ligand-based 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.
DOI: 10.2174/187152611794407782
PubMed: 21303343
Affiliations:
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pubmed:21303343Le document en format XML
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<term>Simulation de dynamique moléculaire</term>
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<front><div type="abstract" xml:lang="en">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) ligand-based 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.</div>
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<Abstract><AbstractText>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) ligand-based 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.</AbstractText>
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