QSAR studies on triazole derivatives as sglt inhibitors via CoMFA and CoMSIA

Zhi, Hui; Zheng, Junxia; Chang, Yiqun; Li, Qingguo; Liao, Guochao; Wang, Qi; Sun, Ping‐Hua

doi:10.1016/j.molstruc.2015.06.004

Cited by 21 publications

(14 citation statements)

References 21 publications

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“…The emergence of the dipole moment is due to the asymmetry in the 3D structure and charge distribution of the molecule, which is consistent with the findings in the literature [12]. The molecular volume, shape, and hydrophobicity of the substituent group affect the pIC 50 of triazole derivatives [12].…”

Section: Descriptor Analysis and Interpretationsupporting

confidence: 90%

“…To check the qualities of the three different methods (MLP NN, SVM, and PPR), we compared the three models presented above with those obtained in literature [12]. Table 4 shows the statistical parameters of the obtained models from the same set of compounds.…”

Section: Comparison Of the Results Obtained By Different Approachesmentioning

confidence: 99%

“…The compounds were divided into training (n = 39) and test (n = 7) sets, and the respective distribution of the sets was identical to that in Ref. [12] to facilitate the comparison of results. The training set was used to build models, and the independent test set was used to evaluate the predictive ability of the models.…”

Section: Data Setmentioning

confidence: 99%

“…Thus, investigating non-glycoside triazole molecules as SGLT2 inhibitor can provide useful information on synthesizing novel potent SGLT2 inhibitors. However, only a few works focus on nonglycoside molecules [10][11][12]. In the literature, only one study reports comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) research on 46 triazole derivatives based on Align Database-Rigid body alignment [12].…”

Section: Introductionmentioning

confidence: 99%

“…However, only a few works focus on nonglycoside molecules [10][11][12]. In the literature, only one study reports comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) research on 46 triazole derivatives based on Align Database-Rigid body alignment [12]. CoMFA and CoMSIA are sensitive to molecular alignment, and the conformation obtained by the Align Database-Rigid body alignment may be far from 'real' bioactive conformation.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Predicting the biological activities of triazole derivatives as SGLT2 inhibitors using multilayer perceptron neural network, support vector machine, and projection pursuit regression models

Yuan

Gao

et al. 2016

Chemometrics and Intelligent Laboratory Systems

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Quantitative structure-activity relationship (QSAR) studies were performed in this work to predict the pIC 50 of non-glycoside sodium-dependent glucose cotransporter-2 (SGLT2) inhibitors (46 triazole derivatives). Four descriptors were selected from the pool of DRAGON descriptors using the enhanced replacement method. Three nonlinear regression methods-multilayer perceptron neural network (MLP NN), support vector machine (SVM), and projection pursuit regression (PPR)-were then used to build the QSAR models. The performance of the obtained models was assessed through the cross-validation and external validation of the test set. PPR produced a better model than MLP NN and SVM with coefficients of determination of 0.962 and 0.871 and root mean square errors of 0.162 and 0.471 for the training and test sets, respectively. This study developed simple and efficient approaches to predict the pIC 50 of triazole derivatives and provided some insights into their structural features for the development of more potential SGLT2 inhibitors.

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Section: Descriptor Analysis and Interpretationsupporting

confidence: 90%

Section: Comparison Of the Results Obtained By Different Approachesmentioning

confidence: 99%

Section: Data Setmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Predicting the biological activities of triazole derivatives as SGLT2 inhibitors using multilayer perceptron neural network, support vector machine, and projection pursuit regression models

Yuan

Gao

et al. 2016

Chemometrics and Intelligent Laboratory Systems

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3D-QSAR, molecular dynamics simulations, and molecular docking studies on pyridoaminotropanes and tetrahydroquinazoline as mTOR inhibitors

Chaube

Bhatt

2017

Mol Divers

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Cancer is a second major disease after metabolic disorders where the number of cases of death is increasing gradually. Mammalian target of rapamycin (mTOR) is one of the most important targets for treatment of cancer, specifically for breast and lung cancer. In the present research work, Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) studies were performed on 50 compounds reported as mTOR inhibitors. Three different alignment methods were used, and among them, distill method was found to be the best method. In CoMFA, leave-one-out cross-validated coefficients [Formula: see text], conventional coefficient [Formula: see text], and predicted correlation coefficient [Formula: see text] values were found to be 0.664, 0.992, and 0.652, respectively. CoMSIA study was performed in 25 different combinations of features, such as steric, electrostatic, hydrogen bond donor, hydrogen bond acceptor, and hydrophobic. From this, a combination of steric, electrostatic, hydrophobic (SEH), and a combination of steric, electrostatic, hydrophobic, donor, and acceptor (SEHDA) were found as best combinations. In CoMSIA (SEHDA), [Formula: see text], [Formula: see text] and [Formula: see text] were found to be 0.646, 0.977, and 0.682, respectively, while in the case of CoMSIA (SEH), the values were 0.739, 0.976, and 0.779, respectively. Contour maps were generated and validated by molecular dynamics simulation-assisted molecular docking study. Highest active compound 19, moderate active compound 15, and lowest active compound 42 were docked on mTOR protein to validate the results of our molecular docking study. The result of the molecular docking study of highest active compound 19 is in line with the outcomes generated by contour maps. Based on the features obtained through this study, six novel mTOR inhibitors were designed and docked. This study could be useful for designing novel molecules with increased anticancer activity.

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Identification of pyrazolotriazinones as potential agents for hyperuricemia treatment by using in vitro and in silico studies

et al. 2020

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Heterocyclic compounds structurally related to purine bases have been described as anticonvulsants, antifungal, antiviral, anticancer, enzyme inhibitors, among others. In this work, pyrazolo [3,4-d][1-3]triazin-4-ones (2) and pyrazolo [4,3-d][1-3] triazin-4-ones (3) derivatives were evaluated as xanthine oxidase (XO) inhibitors. Compounds 3 showed the best activity with IC 50 values range of 0.9-2.9 µM. While the inhibition performance of pyrazolotriazinones was not more active than reference inhibitor allopurinol (IC 50 = 0.247 ± 0.004) µM, these nuclei provide a platform for new and more potent XO inhibitors. Accordingly, molecular modeling methods were carried out to understand the compounds-enzyme binding mode. First, we have performed a qualitative SAR study using the MOE™ SAR tool. This study showed three common scaffolds and the most active was identified. These results are certainly valuable and will be taken into account in future synthesis of structurally related compounds. Furthermore, QSAR 2D and 3D studies were performed and structural requirements for the activity are reported. The obtained results led us to present the structural improvements for the rational design and synthesis of new pyrazolotriazinone derivatives with greater xanthine oxidase inhibitory activity than allopurinol.

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QSAR studies on triazole derivatives as sglt inhibitors via CoMFA and CoMSIA

Cited by 21 publications

References 21 publications

Predicting the biological activities of triazole derivatives as SGLT2 inhibitors using multilayer perceptron neural network, support vector machine, and projection pursuit regression models

Predicting the biological activities of triazole derivatives as SGLT2 inhibitors using multilayer perceptron neural network, support vector machine, and projection pursuit regression models

3D-QSAR, molecular dynamics simulations, and molecular docking studies on pyridoaminotropanes and tetrahydroquinazoline as mTOR inhibitors

Identification of pyrazolotriazinones as potential agents for hyperuricemia treatment by using in vitro and in silico studies

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