Prediction of HIV-1 protease inhibitory activity of 4-hydroxy-5,6-dihydropyran-2-ones: QSAR study

Ravichandran, S.; Vk, Mourya; Agrawal, RK

doi:10.3109/14756366.2010.496364

Cited by 12 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dutchowicz et al 8 reported a QSAR study of some benzoxazinone derivatives but the maximum correlation coefficient of the model is 0.799. The 2D QSAR studies have been carried out for anti-HIV activities of different groups of compounds [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] . Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) have also been carried out for anti-HIV activity of a given set of molecules in rational drug design and its related applications [24][25][26][27][28][29][30][31][32][33][34][35][36][37] .…”

Section: Research Articlementioning

confidence: 99%

Designing hypothesis of substituted benzoxazinones as HIV-1 reverse transcriptase inhibitors: QSAR approach

Veerasamy

Subramaniam

Chean

et al. 2011

Journal of Enzyme Inhibition and Medicinal Chemistry

View full text Add to dashboard Cite

Section: Research Articlementioning

confidence: 99%

Designing hypothesis of substituted benzoxazinones as HIV-1 reverse transcriptase inhibitors: QSAR approach

Veerasamy

Subramaniam

Chean

et al. 2011

Journal of Enzyme Inhibition and Medicinal Chemistry

View full text Add to dashboard Cite

“…In this context, there is a great demand for predictive models to design new drugs with improved properties and diminished side effects [1]. Furthermore, there is also a demand for new methods that replace and reduce the use of laboratory animals [2]. These methods should be used in the design and evaluation of experimental tests and in the selection of appropriate test compounds for validation studies [3].…”

Section: Introductionmentioning

confidence: 99%

An efficient piecewise linear model for predicting activity of caspase-3 inhibitors

et al. 2012

View full text Add to dashboard Cite

Background and purpose of the studyMultimodal distribution of descriptors makes it more difficult to fit a single global model to model the entire data set in quantitative structure activity relationship (QSAR) studies.MethodsThe linear (Multiple linear regression; MLR), non-linear (Artificial neural network; ANN), and an approach based on “Extended Classifier System in Function approximation” (XCSF) were applied herein to model the biological activity of 658 caspase-3 inhibitors.ResultsVarious kinds of molecular descriptors were calculated to represent the molecular structures of the compounds. The original data set was partitioned into the training and test sets by the K-means classification method. Prediction error on the test data set indicated that the XCSF as a local model estimates caspase-3 inhibition activity, better than the global models such as MLR and ANN. The atom-centered fragment type CR2X2, electronegativity, polarizability, and atomic radius and also the lipophilicity of the molecule, were the main independent factors contributing to the caspase-3 inhibition activity.ConclusionsThe results of this study may be exploited for further design of novel caspase-3 inhibitors.

show abstract

“…Considering a myriad of drugs' side effects, viruses' high tendency to mutate and develop drug resistance, and the fact that the last time a HIV protease inhibitor was approved by US FDA in 2006 [22], the design of new protease inhibitors in subnanomolar regime and ideally without side reactions is of paramount importance. In recent years, numerous quantitative structure-activity relationship (QSAR) studies have appeared, trying to connect the structural characteristics of the inhibitor -HIV-1 protease complexes with the inhibition constant and ADMET properties [14,[23][24][25][26][27][28][29]. Amin et al have statistically validated different modeling approaches (Bayesian classification, HQSAR, 3D-QSAR CoMFA and CoMSIA analyses).…”

mentioning

confidence: 99%

Performance of Radial Distribution Function-Based Descriptors in the Chemoinformatic Studies of HIV-1 Protease

et al. 2020

View full text Add to dashboard Cite

Aim: This letter investigates the role of radial distribution function-based descriptors for in silico design of new drugs. Methodology: The multiple linear regression models for HIV-1 protease and its complexes with a series of inhibitors were constructed. A detailed analysis of major atomic contributions to the radial distribution function descriptor weighted by the number of valence shell electrons identified residues Arg8, Asp29 and residues of the catalytic triad as crucial for the correlation with the inhibition constant, together with residues Asp30 and Ile50, whose mutations are known to cause an emergence of drug resistant variants. Conclusion: This study demonstrates an easy and fast assessment of the activity of potential drugs and the derivation of structural information of their complexes with the receptor or enzyme.

show abstract

Prediction of HIV-1 protease inhibitory activity of 4-hydroxy-5,6-dihydropyran-2-ones: QSAR study

Cited by 12 publications

References 16 publications

Designing hypothesis of substituted benzoxazinones as HIV-1 reverse transcriptase inhibitors: QSAR approach

Designing hypothesis of substituted benzoxazinones as HIV-1 reverse transcriptase inhibitors: QSAR approach

An efficient piecewise linear model for predicting activity of caspase-3 inhibitors

Performance of Radial Distribution Function-Based Descriptors in the Chemoinformatic Studies of HIV-1 Protease

Contact Info

Product

Resources

About