Multivariate QSAR study of 4,5-dihydroxypyrimidine carboxamides as HIV-1 integrase inhibitors

Melo, Eduardo Borges de; Ferreira, Márcia M. C.

doi:10.1016/j.ejmech.2009.03.001

Cited by 26 publications

(21 citation statements)

References 44 publications

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“…Standard error of prediction should be as low as possible for good predictive model was calculated by following equation (De Melo and Ferriea, 2009):…”

Section: Evaluation Of Predictive Ability Of Qsar Modelsmentioning

confidence: 99%

QSAR study of a series of 2,3-dihydroimidazo[1,2-c]pyrimidines as antibacterial agents

et al. 2011

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Quantitative structure activity relationship has been established between the various physiochemical parameters of a series of 2,3-dihydroimidazo[1,2-c]pyrimidines and its antibacterial activity against gram-positive and gram-negative bacteria. Genetic function approximation (GFA) technique was used to identify the descriptors that have influence on biological activity. The most influencing molecular descriptors identified include geometric, topological, and quantum mechanical descriptors. Statistical qualities (r 2 , r cv 2 , r pred 2 , r m 2 , k, k 0 , SEP) indicated the significance and predictive power of the developed models. The study indicates that antibacterial activity on grampositive bacteria can be improved by bulky electron releasing group whereas on gram-negative bacteria by electron withdrawing group.

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“…Standard error of prediction should be as low as possible for good predictive model was calculated by following equation (De Melo and Ferriea, 2009):…”

Section: Evaluation Of Predictive Ability Of Qsar Modelsmentioning

confidence: 99%

QSAR study of a series of 2,3-dihydroimidazo[1,2-c]pyrimidines as antibacterial agents

et al. 2011

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“…HIV-1 integrase does not have any human homolog, and it consequently is one of the best candidate for designing and developing new inhibitors/drugs in the AIDS therapy (Greene et al, 2008). Diketo acids , dihydroxypyrimidine carboxamide (de Melo and Ferreira, 2009), quinolinone acids (Sato et al, 2009), and naphthyridine classes (Melamed et al, 2008;Johnson et al, 2011) have been extensively studied for the HIV-1 integrase inhibition activity. Raltegravir and Elvitegravir (Fig.…”

Section: Introductionmentioning

confidence: 99%

“…The QSAR has crucial role in the construction novel and potent lead compounds as well as saving the time and cost for better prediction of new compounds activity (Verma et al, 2010). It has been broadly applied for the aim of designing new anti-HIV drugs based on integrase inhibition (Makhija and Kulkarni, 2002;Yuan and Parrill, 2002;Saiz-Urra et al, 2007;de Melo and Ferreira, 2009). Already, the computational QSAR-based methods were widely used to develop potential Fig.…”

Section: Introductionmentioning

confidence: 99%

Developing 2D-QSAR models for naphthyridine derivatives against HIV-1 integrase activity

et al. 2014

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HIV-1 integrase is an extremely important nominee in developing new and effective drugs especially naphthyridine compounds against acquired immune deficiency syndrome. The quantitative structure-activity relationship (QSAR) modeling is the most powerful method in computer-aided drug design and will be used to help the design of new naphthyridine derivatives. Different computational 2D-QSAR procedures applied to predict the relationship between the computational descriptors of naphthyridine derivatives with their HIV-1 integrase inhibition activities. Four different models including stepwise-MLR, consensus stepwise-MLR, GAPLS-MLR, and consensus GAPLS-MLR with appropriate correlation between the calculated and experimental biological activities (pIC 50 ) against HIV-1 integrase were generated. Predictive QSAR models were obtained with R training 2 values of 0.848, 0.862, 0.709, and 0.751 as well as R test 2 values of 0.521, 0.651, 0.502, and 0.775 for stepwise-MLR, consensus stepwise-MLR, GAPLS-MLR, and consensus GAPLS-MLR models, respectively. QSAR models are high efficiency in prediction of the pIC 50 in comparison with other models because of concerning the combination of ''quantum and molecular mechanical'' descriptors. Combination of ''quantum'' and ''molecular mechanical'' descriptors improved our models with high efficient test set activity prediction potency. The obtained results provided useful information for understanding the effects of polarizability, electronegativity, and especially functional groups such as aromatic nitrogens that are important for the activities of naphthyridine compounds. The developed QSAR models will be efficient for the rational design of potent naphthyridine derivatives against HIV-1 integrase activity.

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“…8,9 On the other hand, the use of quantitative structure-activity relationship (QSAR) methods has been broadly applied with the aim of designing new anti-HIV-1 drugs based on integrase inhibition. [12][13][14][15][16] The QSAR model attempts to¯nd the mathematical relationships between molecular structure and biological activity. 17,18 The computer-aided drug design Table 2 are only di®erent in R 1 , R 2 and R 3 substituents of A and B Aryl rings.…”

Section: Introductionmentioning

confidence: 99%

Quantitative structure–activity relationships study of potent pyridinone scaffold derivatives as HIV-1 integrase inhibitors with therapeutic applications

Barzegar

Hamidi

2017

J. Theor. Comput. Chem.

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Human immunode¯ciency virus-1 (HIV-1) integrase appears to be a crucial target for developing new anti-HIV-1 therapeutic agents. Di®erent quantitative structure-activity relationships (QSARs) algorithms have been used in order to develop e±cient model(s) to predict the activity of new pyridinone derivatives against HIV-1 integrase. Multiple linear regression (MLR) and combined principal component analysis (PCA) with MLR have been applied to build QSAR models for a set of new pyridinone derivatives as potent anti-HIV-1 therapeutic agents. Four di®erent approaches based on MLR method including; concrete-MLR, stepwise-MLR, concrete PCA-MLR and stepwise PCA-MLR were utilized for this aim. Twenty two di®erent sets of descriptors containing 1613 descriptors were constructed for each optimized molecule. Comparison between predictability of the \concrete" and \stepwise" procedure in two di®erent algorithms of MLR and PCA models indicated the advantage of the stepwise procedure over that of the simple concrete method. Although the PCA was employed for dimension reduction, using stepwise PCA-MLR model showed that the method has higher ability to predict the compounds' activity. The stepwise PCA-MLR model showed highly validated statistical results both in¯tting and prediction processes (R 2 test ¼ 0:78 and Q 2 ¼ 0:80). Therefore, using stepwise PCA approach is suitable to remove ine®ective descriptors, which results in remaining e±cient descriptors for building good predictability stepwise PCA-MLR. The stepwise hybrid approach of PCA-MLR may be useful in derivation of highly predictive and interpretable QSAR models.

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Multivariate QSAR study of 4,5-dihydroxypyrimidine carboxamides as HIV-1 integrase inhibitors

Cited by 26 publications

References 44 publications

QSAR study of a series of 2,3-dihydroimidazo[1,2-c]pyrimidines as antibacterial agents

QSAR study of a series of 2,3-dihydroimidazo[1,2-c]pyrimidines as antibacterial agents

Developing 2D-QSAR models for naphthyridine derivatives against HIV-1 integrase activity

Quantitative structure–activity relationships study of potent pyridinone scaffold derivatives as HIV-1 integrase inhibitors with therapeutic applications

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