3D‐QSAR and Docking Studies on the HEPT Derivatives of HIV‐1 Reverse Transcriptase

Latha, Ramaswamy Sree; Vijayaraj, R.; Singam, Ettayapuram Ramaprasad Azhagiya; Chitra, K.; Subramanian, V.

doi:10.1111/j.1747-0285.2011.01162.x

Cited by 7 publications

(1 citation statement)

References 53 publications

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“…One derivative, (5E)-3-(2-aminoethyl)-5-(2-thienylmethylene)-1,3-thiazolidine-2,4-dione (CID 3087795), was discovered to be a promising inhibitor for HIV-1 RT with a minimum energy score and the highest number of interactions with active site residues (6). Molecular docking is also widely used in SAR studies, as a way to evaluate the anti-viral activity of newly discovered or synthesized compounds (7)(8)(9)(10)(11)(12)(13)(14)(15)(16).…”

Section: Reverse Transcriptasementioning

confidence: 99%

Anti-HIV Drug Development Through Computational Methods

Zhang

Yuan

2014

AAPS J

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Abstract. Although highly active antiretroviral therapy (HAART) is effective in controlling the progression of AIDS, the emergence of drug-resistant strains increases the difficulty of successful treatment of patients with HIV infection. Increasing numbers of patients are facing the dilemma that comes with the running out of drug combinations for HAART. Computational methods play a key role in anti-HIV drug development. A substantial number of studies have been performed in anti-HIV drug development using various computational methods, such as virtual screening, QSAR, molecular docking, and homology modeling, etc. In this review, we summarize recent advances in the application of computational methods to anti-HIV drug development for five key targets as follows: reverse transcriptase, protease, integrase, CCR5, and CXCR4. We hope that this review will stimulate researchers from multiple disciplines to consider computational methods in the anti-HIV drug development process.

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Section: Reverse Transcriptasementioning

confidence: 99%

Anti-HIV Drug Development Through Computational Methods

Zhang

Yuan

2014

AAPS J

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Docking field‐based QSAR and pharmacophore studies on the substituted pyrimidine derivatives targeting HIV‐1 reverse transcriptase

et al. 2017

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HIV-1 reverse transcriptase (RT) is one of the most important enzymes required for viral replication, thus acting as an attractive target for antiretroviral therapy. Pyrimidine analogues reportedly have selective inhibition on HIV-1 RT with favorable antiviral activities in our previous study. To further explore the relationship between inhibitory activity and pharmacophoric characteristics, field-based QSAR models were generated and validated using Schrodinger Suite (correlation coefficient of .8078, cross-validated value of 0.5397 for training set and Q of 0.4669, Pearson's r of .7357 for test set). Docking, pocket surfaces, and pharmacophore study were also investigated to define the binding pattern and pharmacophoric features, including (i) π-π interaction with residue Tyr181, Tyr188, and Trp229 and p-π interaction with His235 and (ii) hydrogen bond with residue Lys101 and halogen bond with residue Tyr188. The pharmacophore features of six-point hypothesis AADRRR.184, AAADRR.38, and AADRRR.26 further complimented to the docking and QSAR results. We also found that the protein-ligand complex exhibited high relative binding free energy. These observations could be potentially utilized to guide the rational design and optimization of novel HIV-1 RT inhibitors.

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HIVprotI: an integrated web based platform for prediction and design of HIV proteins inhibitors

et al. 2018

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A number of anti-retroviral drugs are being used for treating Human Immunodeficiency Virus (HIV) infection. Due to emergence of drug resistant strains, there is a constant quest to discover more effective anti-HIV compounds. In this endeavor, computational tools have proven useful in accelerating drug discovery. Although methods were published to design a class of compounds against a specific HIV protein, but an integrated web server for the same is lacking. Therefore, we have developed support vector machine based regression models using experimentally validated data from ChEMBL repository. Quantitative structure activity relationship based features were selected for predicting inhibition activity of a compound against HIV proteins namely protease (PR), reverse transcriptase (RT) and integrase (IN). The models presented a maximum Pearson correlation coefficient of 0.78, 0.76, 0.74 and 0.76, 0.68, 0.72 during tenfold cross-validation on IC50 and percent inhibition datasets of PR, RT, IN respectively. These models performed equally well on the independent datasets. Chemical space mapping, applicability domain analyses and other statistical tests further support robustness of the predictive models. Currently, we have identified a number of chemical descriptors that are imperative in predicting the compound inhibition potential. HIVprotI platform (http://bioinfo.imtech.res.in/manojk/hivproti) would be useful in virtual screening of inhibitors as well as designing of new molecules against the important HIV proteins for therapeutics development.Electronic supplementary materialThe online version of this article (10.1186/s13321-018-0266-y) contains supplementary material, which is available to authorized users.

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3D‐QSAR and Docking Studies on the HEPT Derivatives of HIV‐1 Reverse Transcriptase

Cited by 7 publications

References 53 publications

Anti-HIV Drug Development Through Computational Methods

Anti-HIV Drug Development Through Computational Methods

Docking field‐based QSAR and pharmacophore studies on the substituted pyrimidine derivatives targeting HIV‐1 reverse transcriptase

HIVprotI: an integrated web based platform for prediction and design of HIV proteins inhibitors

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