Journal of Molecular Modeling

, 23:272 | Cite as

Docking-assisted 3D-QSAR studies on xanthones as α-glucosidase inhibitors

  • Xuehua Zheng
  • Siyuan Zhou
  • Chen Zhang
  • Deyan Wu
  • Hai-Bin Luo
  • Yinuo WuEmail author
Original Paper


Recently, a series of xanthone analogues has been identified as α-glucosidase inhibitors. To provide deeper insight into the three-dimensional (3D) structural requirements for the activities of these molecules, CoMFA and CoMSIA approaches were employed on 54 xanthones to construct 3D-QSAR models. Their bioactive conformations were first investigated by docking studies and optimized by subsequent molecular dynamics (MD) simulations using the homology modeled structure of the target protein. Based on the docking/MD-determined conformers, 3D-QSAR studies generated several significant models in terms of 47 molecules as the training set. The best model (CoMSIA-SHA) yielded q 2 of 0.713, r 2 of 0.967 and F of 140.250. The robustness of the model was further externally confirmed by a test set of the remaining molecules (q 2 = 0.793, r 2 = 0.902, and k = 0.905). Contour maps provided much information for future design and optimization of new compounds with high inhibitory activities towards α-glucosidase.

Graphical Abstract

CoMSIA/SHA contour map of xanthone α-glucosidase inhibitor


Xanthone derivatives α-glucosidase Docking 3D-QSAR Homology modeling 



This work was supported by the Natural Science Foundation of China (21572279, 81373258, 81522041, and 81602968); the Medical Scientific Research Foundation of Guangdong Province (A2016201); the Natural Science Foundation of Guangdong Province (2014A020210009 and 2016A030313589); Science Foundation of Guangzhou City (2014 J4100165); Science and Technology Program of Guangzhou (201605101030072 and 201707010049); Guangdong Province Higher Vocational Colleges & Schools Pearl River Scholar Funded Scheme (2016); and Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase).

Supplementary material

894_2017_3438_MOESM1_ESM.doc (112 kb)
ESM 1 (DOC 111 kb)


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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Xuehua Zheng
    • 1
  • Siyuan Zhou
    • 2
  • Chen Zhang
    • 2
  • Deyan Wu
    • 2
  • Hai-Bin Luo
    • 2
    • 3
  • Yinuo Wu
    • 2
    Email author
  1. 1.School of Pharmaceutical SciencesGuangzhou Medical UniversityGuangzhouChina
  2. 2.School of Pharmaceutical SciencesSun Yat-sen UniversityGuangzhouChina
  3. 3.Collaborative Innovation Center of High Performance ComputingNational University of Defense TechnologyChangshaChina

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