Journal
DATA IN BRIEF
Volume 9, Issue -, Pages 35-42Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.dib.2016.08.004
Keywords
Molecular modelling; QSAR; Multiple linear regression; P-glycoprotein; Flavonoids; Herb-drug interaction
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Funding
- Graduate School, Chiang Mai University, Thailand [32/2559]
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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, R-adj(2)=0.900, 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. (C) 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/40).
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