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

Zakariazadeh, Mostafa; Barzegar, Abolfazl; Soltani, Somaieh; Aryapour, Hassan

doi:10.1007/s00044-014-1305-5

Cited by 21 publications

(16 citation statements)

References 52 publications

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“…P_VSA_MR_6 has also been used for modeling of skin permeability (95), whereas we have identified the use of Chi1_EA(dm) only for the QSPR modeling of fluorescence properties of a number of fluorescent dyes (96). The aromatic nitrogen (N-073) has been shown to correlate positively with HIV-1 integrase activity inhibition (97) and negatively with the inhibition of the fibroblast growth factor (FGFR) (98). We found no previous reports on the use of the Balaban distance connectivity index (J_D) in other models in the biological field, neither of the F05[C-N].…”

Section: Discussionmentioning

confidence: 99%

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-Src Tyrosine Kinase

Ancuceanu

Tamba

Stoicescu³

et al. 2019

Preprint

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Prototype of a family of at least nine members, c-src tyrosine kinase is a therapeutically interesting target, because its inhibition might be of interest not only in a number of malignancies, but also in a diverse array of conditions, from neurodegenerative pathologies to certain viral infections. Computational methods in drug discovery are considerably cheaper than conventional methods and offer opportunities of screening very large numbers of compounds in conditions that would be simply impossible within the wet lab experimental settings. We have explored the use of global QSAR models and molecular ligand docking in the discovery of new c-src tyrosine kinase inhibitors. Using a data set of 1038 compounds from ChEMBL and 19 blocks of molecular descriptors, we have developed over 200 QSAR classification models, based on six machine learning algorithms and 17 feature selection methods. We have selected 49 with reasonably good performance (positive predictive value and balanced accuracy higher than 70% in nested cross validation) and the models were assembled by stacking with a simple majority vote and used for the virtual screening of over the “named” ZINC data set (over 100,000 compounds). 744 compounds were predicted by at least 50% of the QSAR models as active, 147 compounds were within the applicability domain and predicted by at least 75% of the models to be active. The latter 147 compounds were submitted to molecular ligand docking using Vina and Ledock, and a number of 90 were predicted to be active based on the binding energy. External data from CHEMBL and PUBCHEM confirmed that at least 7.83% (in the case of QSAR) or 6.67% (in the case of integrated QSAR and molecular docking) of the compounds are active on the c-src target.

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Section: Discussionmentioning

confidence: 99%

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-Src Tyrosine Kinase

Ancuceanu

Tamba

Stoicescu³

et al. 2019

Preprint

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“…The MLR is a commonly used method in QSAR due to its simplicity, transparency, reproducibility, and easy interpretability. [28][29][30] The stepwise algorithm is a method of¯tting regression models with backward elimination and forward selection. In forward selection, there is a possibility that the selected feature at an early step may become worthless at a later step.…”

Section: Regression Analysis For Descriptor Selectionmentioning

confidence: 99%

“…28 The biological activities (EC 50 Þ of di®erent 53 compounds were sorted and subsequently classi¯ed under the \Gaussian" series for the training and testing QSARs, respectively. The Pearson correlation coe±cient (R pearson Þ ranges between À1.0 and 1.0.…”

Section: Selection Of Training and Test Setsmentioning

confidence: 99%

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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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“…9 Indeed, several regression QSAR models for HIV-1 INSTIs have been reported based on techniques such as molecular docking 10 , comparative field molecular analysis 11 (COMFA) and comparative molecular similarity indices analysis 12 (COMSIA). However, these QSAR models are specific to particular classes of HIV-1 INSTIs such as carboxylic acid derivatives (trained on 62 compounds) 13 , curcumine (trained on 29 compounds) 14 , pyridinone (trained on 53 compounds) 15 , -diketo-acids (trained on 37 on compounds) 16 and napthyridine (trained on 50 compounds) 17 . As far as we are concerned, our study is the first large-scale QSAR study (trained on 1417 compounds) that covers a broad chemical structure diversity.…”

Section: Introductionmentioning

confidence: 99%

Regression QSAR Models for Predicting HIV-1 Integrase Inhibitors

Xuan

Lee

Rahman

et al. 2021

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The Human Immunodeficiency Virus (HIV) infection is a global pandemic that has claimed 33 million lives to date. One of the most efficacious treatment for naïve or pre-treated HIV patients is with the HIV integrase strand transfer inhibitors (INSTIs). However, given that HIV treatment is lifelong, the emergence of HIV-1 strains resistant to INSTIs is an imminent challenge. In this work, we showed two best regression QSAR models that were constructed using a boosted Random Forest algorithm (r2 = 0.998, q210CV = 0.721, q2external_test = 0.754) and a boosted K* algorithm (r2 = 0.987, q210CV = 0.721, q2external_test = 0.758) to predict the pIC50 values of INSTIs. Subsequently, the regression QSAR models were deployed against the Drugbank database for drug repositioning. The top ranked compounds were further evaluated for their target engagement activity using molecular docking studies and their potential as INSTIs evaluated from our literature search. Our study offers the first example of a large-scale regression QSAR modelling effort for discovering highly active INSTIs to combat HIV infection.

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

Cited by 21 publications

References 52 publications

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-Src Tyrosine Kinase

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-Src Tyrosine Kinase

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

Regression QSAR Models for Predicting HIV-1 Integrase Inhibitors

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