Abstract
The human immunodeficiency virus type 1 (HIV-1) integrase is an emerging target for novel antiviral drugs. Quantitative structure-activity relationship (QSAR) models for HIV-1 integrase inhibitors have been developed to understand the protein-ligand interactions to aid in the design of more effective analogs. This review paper presents a comprehensive overview of the computational modeling methods and results of QSAR models of HIV-1 integrase inhibitors published in 2005-2010. These QSAR models are classified according to the generation of molecular descriptors: 2D-QSAR, 3D-QSAR, and 4D-QSAR. Linear and non-linear modeling methods have been applied to derive these QSAR models, with the majority of the models derived from linear statistical methods such as multiple linear regression and partial least squares. While each of the published QSAR models have provided insight on the distinct chemical features of HIV-1 integrase inhibitors crucial for biological activity, only a few models have been used to propose and synthesize new HIV-1 integrase inhibitors. This study highlights the need for collaboration between computational and experimental chemists to utilize and improve these QSAR models to guide the design of the next generation of HIV-1 integrase inhibitors.
Keywords: Cheminformatics, QSAR, AIDS, HIV-1 integrase inhibitors, computational modeling, HIV-1 integrase QSAR review
Current Computer-Aided Drug Design
Title:Computational Modeling Methods for QSAR Studies on HIV-1 Integrase Inhibitors (2005-2010)
Volume: 8 Issue: 4
Author(s): Gene M. Ko, A. Srinivas Reddy, Rajni Garg, Sunil Kumar and Ahmad R. Hadaegh
Affiliation:
Keywords: Cheminformatics, QSAR, AIDS, HIV-1 integrase inhibitors, computational modeling, HIV-1 integrase QSAR review
Abstract: The human immunodeficiency virus type 1 (HIV-1) integrase is an emerging target for novel antiviral drugs. Quantitative structure-activity relationship (QSAR) models for HIV-1 integrase inhibitors have been developed to understand the protein-ligand interactions to aid in the design of more effective analogs. This review paper presents a comprehensive overview of the computational modeling methods and results of QSAR models of HIV-1 integrase inhibitors published in 2005-2010. These QSAR models are classified according to the generation of molecular descriptors: 2D-QSAR, 3D-QSAR, and 4D-QSAR. Linear and non-linear modeling methods have been applied to derive these QSAR models, with the majority of the models derived from linear statistical methods such as multiple linear regression and partial least squares. While each of the published QSAR models have provided insight on the distinct chemical features of HIV-1 integrase inhibitors crucial for biological activity, only a few models have been used to propose and synthesize new HIV-1 integrase inhibitors. This study highlights the need for collaboration between computational and experimental chemists to utilize and improve these QSAR models to guide the design of the next generation of HIV-1 integrase inhibitors.
Export Options
About this article
Cite this article as:
M. Ko Gene, Srinivas Reddy A., Garg Rajni, Kumar Sunil and R. Hadaegh Ahmad, Computational Modeling Methods for QSAR Studies on HIV-1 Integrase Inhibitors (2005-2010), Current Computer-Aided Drug Design 2012; 8 (4) . https://dx.doi.org/10.2174/157340912803519624
DOI https://dx.doi.org/10.2174/157340912803519624 |
Print ISSN 1573-4099 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-6697 |
- Author Guidelines
- Graphical Abstracts
- Fabricating and Stating False Information
- Research Misconduct
- Post Publication Discussions and Corrections
- Publishing Ethics and Rectitude
- Increase Visibility of Your Article
- Archiving Policies
- Peer Review Workflow
- Order Your Article Before Print
- Promote Your Article
- Manuscript Transfer Facility
- Editorial Policies
- Allegations from Whistleblowers
Related Articles
-
Management and Treatment of Cardiovascular Diseases in the Elderly
Current Pharmacogenomics and Personalized Medicine Preliminary Identification of Hamamelitannin and Rosmarinic Acid as COVID-19 Inhibitors Based on Molecular Docking
Letters in Drug Design & Discovery Expression of Short Peptide by an Improved Isocaudamer Tandem Repeat Strategy
Protein & Peptide Letters Molecular Docking and Quantum Studies of Lawsone Dimers Derivatives: New Investigation of Antioxidant Behavior and Antifungal Activity
Current Topics in Medicinal Chemistry Inhibitors of HIV-1 Protease: Current State of the Art 10 Years After their Introduction. From Antiretroviral Drugs to Antifungal, Antibacterial and Antitumor Agents Based on Aspartic Protease Inhibitors
Current Medicinal Chemistry The Detection of Pb<sup>2+</sup> Using Glutathione Capped Mn Doped ZnS QDs
Current Analytical Chemistry Recent Advancement in Discovery and Development of Natural Product Combretastatin-inspired Anticancer Agents
Anti-Cancer Agents in Medicinal Chemistry The Concept of Titration can be Transposed to Fluid Management. But does is Change the Volumes? Randomised Trial on Pleth Variability Index During Fast-Track Colonic Surgery
Current Clinical Pharmacology New C2- and N3-Modified Thieno[2,3-d]Pyrimidine Conjugates with Cytotoxicity in the Nanomolar Range
Anti-Cancer Agents in Medicinal Chemistry Post-COVID-19 Pulmonary Hypertension: How it May Physiologically Affect Exercise Training
Current Respiratory Medicine Reviews Efficient Synthesis of Five and Six Member Sultam
Letters in Organic Chemistry Cannabinoid Type 2 Receptor as a Target for Chronic - Pain
Mini-Reviews in Medicinal Chemistry Endothelin Receptor Antagonists: Another Potential Alternative for Cardiovascular Diseases
Current Drug Targets - Cardiovascular & Hematological Disorders Computational Strategies to Predict Effect of P-Glycoprotein Transporter Efflux and Minimize its Impact on the Penetration of Drugs into the Central Nervous System (CNS)
Current Computer-Aided Drug Design A Narrative Review of Recent Studies on the Role of Vitamin D in the Prevention of Cardiac and Renal Risk and Additional Considerations for COVID-19 Vulnerability
Current Vascular Pharmacology DNA Microarrays - An Armory for Combating Infectious Diseases in the New Century
Infectious Disorders - Drug Targets Synthetic and Natural Monoamine Oxidase Inhibitors as Potential Lead Compounds for Effective Therapeutics
Central Nervous System Agents in Medicinal Chemistry Recent Advances in Protein Tyrosine Phosphatase 1B Targeted Drug Discovery for Type II Diabetes and Obesity
Current Drug Targets Antihistamines as Important Tools for Regulating Inflammation
Current Medicinal Chemistry - Anti-Inflammatory & Anti-Allergy Agents Cardiovascular Considerations of Remdesivir and Favipiravir in the Treatment of COVID-19
Cardiovascular & Hematological Disorders-Drug Targets