Dataset Information

3D-QSAR modelling dataset of bioflavonoids for predicting the potential modulatory effect on P-glycoprotein activity.

ABSTRACT: The data is obtained from exploring the modulatory activities of bioflavonoids on P-glycoprotein function by ligand-based approaches. Multivariate Linear-QSAR models for predicting the induced/inhibitory activities of the flavonoids were created. Molecular descriptors were initially used as independent variables and a dependent variable was expressed as pFAR. The variables were then used in MLR analysis by stepwise regression calculation to build the linear QSAR data. The entire dataset consisted of 23 bioflavonoids was used as a training set. Regarding the obtained MLR QSAR model, R of 0.963, R (2)=0.927, [Formula: see text], SEE=0.197, F=33.849 and q (2)=0.927 were achieved. The true predictabilities of QSAR model were justified by evaluation with the external dataset (Table 4). The pFARs of representative flavonoids were predicted by MLR QSAR modelling. The data showed that internal and external validations may generate the same conclusion.

SUBMITTER: Wongrattanakamon P

PROVIDER: S-EPMC5011158 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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3D-QSAR modelling dataset of bioflavonoids for predicting the potential modulatory effect on P-glycoprotein activity.

Wongrattanakamon Pathomwat P Lee Vannajan Sanghiran VS Nimmanpipug Piyarat P Jiranusornkul Supat S

Data in brief 20160804

The data is obtained from exploring the modulatory activities of bioflavonoids on P-glycoprotein function by ligand-based approaches. Multivariate Linear-QSAR models for predicting the induced/inhibitory activities of the flavonoids were created. Molecular descriptors were initially used as independent variables and a dependent variable was expressed as pFAR. The variables were then used in MLR analysis by stepwise regression calculation to build the linear QSAR data. The entire dataset consiste ...[more]

PMID: 27626051

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Project description:Influenza virus disease is one of the most infectious diseases responsible for many human deaths, and the high mutability of the virus causes drug resistance effects in recent times. As such, it became necessary to explore more inhibitors that could avert future influenza pandemics. The present research utilized some in-silico modelling concepts such as 2D-QSAR, 3D-QSAR, molecular docking simulation, and ADMET predictions on some 5-benzyl-4-thiazolinone derivatives as influenza neuraminidase (NA) inhibitors. The 2D-QSAR modelling results revealed GFA-MLR ( Rtrain2 =0.8414, Q2 = 0.7680) and GFA-ANN ( Rtrain 2 =0.8754, Q2 = 0.8753) models with the most relevant descriptors (MATS3i, SpMax5_Bhe, minsOH and VE3_D) for predicting the inhibitory activities of the molecules which has passed the global criteria of accepting QSAR models. The results of the 3D-QSAR modelling results showed that CoMFA_ES ( Rtrain 2 =0.9030, Q2 = 0.5390) and CoMSIA_EA ( Rtrain2 =0.880, Q2 = 0.547) models are having good predicting ability among other developed models. The molecules were virtually screened via molecular docking simulation with the active site of NA protein receptor (pH1N1) which confirms their resilient potency when compared with zanamivir standard drug. Molecule 11 as the most potent molecule formed more H-bond interactions with the key residues such as TRP178, ARG152, ARG292, ARG371, and TYR406 that triggered the catalytic reactions for NA inhibition. Furthermore, six (6) molecules (9, 10, 11, 17, 22, and 31) with relatively high inhibitory activities and docking scores were identified as the possible leads for in-silico exploration of novel NA inhibitors. The drug-likeness and ADMET predictions of the lead molecules revealed non-violation of Lipinski's rule and good pharmacokinetic profiles respectively, which are important guidelines for rational drug design. Hence, the outcome of this study overlaid a solid foundation for the in-silico design and exploration of novel NA inhibitors with improved potency.

Project description:BackgroundInfluenza virus disease remains one of the most contagious diseases that aided the deaths of many patients, especially in this COVID-19 pandemic era. Recent discoveries have shown that the high prevalence of influenza and SARS-CoV-2 coinfection can rapidly increase the death rate of patients. Hence, it became necessary to search for more potent inhibitors for influenza disease therapy. The present study utilized some computational modeling concepts such as 2D-QSAR, 3D-QSAR, molecular docking simulation, and ADMET predictions of some 1,3-thiazine derivatives as inhibitors of influenza neuraminidase (NA).ResultsThe 2D-QSAR modeling results showed GFA-MLR ( Rtrain2 = 0.9192, Q 2 = 0.8767, R 2 adj = 0.8991, RMSE = 0.0959, Rtest2 = 0.8943, Rpred2 = 0.7745) and GFA-ANN ( Rtrain2 = 0.9227, Q 2 = 0.9212, RMSE = 0.0940, Rtest2 = 0.8831, Rpred2 = 0.7763) models with the computed descriptors as ATS7s, SpMax5_Bhv, nHBint6, and TDB9m for predicting the NA inhibitory activities of compounds which have passed the global criteria of accepting QSAR model. The 3D-QSAR modeling was carried out based on the comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA). The CoMFA_ES ( Rtrain2 = 0.9620, Q 2 = 0.643) and CoMSIA_SED ( Rtrain2 = 0.8770, Q 2 = 0.702) models were found to also have good and reliable predicting ability. The compounds were also virtually screened based on their binding scores via molecular docking simulations with the active site of the NA (H1N1) target receptor which also confirms their resilient potency. Four potential lead compounds (4, 7, 14, and 15) with the relatively high inhibitory rate (> 50%) and docking (> - 6.3 kcal/mol) scores were identified as the possible lead candidates for in silico exploration of improved anti-influenza agents.ConclusionThe drug-likeness and ADMET predictions of the lead compounds revealed non-violation of Lipinski's rule and good pharmacokinetic profiles as important guidelines for rational drug design. Hence, the outcome of this research set a course for the in silico design and exploration of novel NA inhibitors with improved potency.

Dataset Information

3D-QSAR modelling dataset of bioflavonoids for predicting the potential modulatory effect on P-glycoprotein activity.

Publications

3D-QSAR modelling dataset of bioflavonoids for predicting the potential modulatory effect on P-glycoprotein activity.

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