A simple and robust model to predict the inhibitory activity of α-glucosidase inhibitors through combined QSAR modeling and molecular docking techniques


Quantitative structure–activity relationships (QSAR) and molecular docking studies have been performed on a series of 35 α-glucosidase inhibitory derivatives. The QSAR models have been developed by genetic algorithm-multiple linear regression (GA-MLR) and least squares-support vector machine (LS-SVM) methods to correlate the conformational descriptors to the inhibitory activity. The obtained models with 5 descriptors were validated and illustrated to be statistically significant. They had desirable prediction based on squared correlation coefficient (R2), cross-validated correlation coefficient (Q2), root-mean-squares error (RMSE) and Fisher (F) parameters (R2 = 0.951, Q2 = 0.931, RMSE = 0.121, and F = 114.629 for GA-MLR model, and R2 = 0.989, Q2 = 0.987, RMSE = 0.056 and F = 543.754 for LS-SVM model). The crucial descriptor named DELS was explored to have the highest correlation with the inhibitory activity and thus has been chosen to build a simple model. The QSAR model developed with this mono-descriptor showed appropriate results of the predicted model using LS-SVM method (R2 = 0.888, Q2 = 0.872, RMSE = 0.185 and F = 221.459). Also, molecular docking which focuses on the interaction between ligands and α-glucosidase in the protein active site considered different binding positions to find the best binding mode. It helped the QSAR study to propose more comprehensive details of the compounds structures and was used to design more active compounds. The most active designed compound had a high inhibitory activity of 9.22 that can be proposed for the treatment of diabetes type 2.

Graphic abstract

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17


  1. 1.

    Imran S, Taha M, Ismail NH, Kashif SM, Rahim F, Jamil W, Hariono M, Yusuf M, Wahab H (2015) Synthesis of novel flavone hydrazones: in-vitro evaluation of α-glucosidase inhibition, QSAR analysis and docking studies. Eur J Med Chem 105:156–170. https://doi.org/10.1016/j.ejmech.2015.10.017

    CAS  Article  PubMed  Google Scholar 

  2. 2.

    Goldenberg RM (2011) Management of unmet needs in type 2 diabetes mellitus: the role of incretin agents. Can J Diabetes 35(5):518–527. https://doi.org/10.1016/S1499-2671(11)80008-0

    Article  PubMed  Google Scholar 

  3. 3.

    Narender T, Madhur G, Jaiswal N, Agrawal M, Maurya CK, Rahuja N, Srivastava AK, Tamrakar AK (2013) Synthesis of novel triterpene and N-allylated/N-alkylated niacin hybrids as α-glucosidase inhibitors. Eur J Med Chem 63:162–169. https://doi.org/10.1016/j.ejmech.2013.01.053

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    Dinparast L, Valizadeh H, Bahadori MB, Soltani S, Asghari B, Rashidi MR (2016) Design, synthesis, α-glucosidase inhibitory activity, molecular docking and QSAR studies of benzimidazole derivatives. J Mol Struct 1114:84–94. https://doi.org/10.1016/j.molstruc.2016.02.005

    CAS  Article  Google Scholar 

  5. 5.

    Park H, Hwang KY, Kim YH, Oh KH, Lee JY, Kim K (2008) Discovery and biological evaluation of novel α-glucosidase inhibitors with in vivo antidiabetic effect. Bioorg Med Chem Lett 18(13):3711–3715. https://doi.org/10.1016/j.bmcl.2008.05.056

    CAS  Article  PubMed  Google Scholar 

  6. 6.

    Park H, Hwang KY, Oh KH, Kim YH, Lee JY, Kim K (2008) Discovery of novel α-glucosidase inhibitors based on the virtual screening with the homology-modeled protein structure. Bioorg Med Chem 16(1):284–292. https://doi.org/10.1016/j.bmc.2007.09.036

    CAS  Article  PubMed  Google Scholar 

  7. 7.

    Asadollahi-Baboli M, Dehnavi S (2018) Docking and QSAR analysis of tetracyclic oxindole derivatives as α-glucosidase inhibitors. Comput Biol Chem 76:283–292. https://doi.org/10.1016/j.compbiolchem.2018.07.019

    CAS  Article  PubMed  Google Scholar 

  8. 8.

    Scott LJ, Spencer CM (2000) Miglitol: a review of its therapeutic potential in type 2 diabetes. Drugs 59(3):521–549. https://doi.org/10.2165/00003495-200059030-00012

  9. 9.

    Wang SL (2018) New novel α-glucosidase inhibitors produced by microbial conversion. Process Biochem 65:228–232. https://doi.org/10.1016/j.procbio.2017.11.016

    CAS  Article  Google Scholar 

  10. 10.

    Channar PA, Saeed A, Larik FA, Rashid S, Iqbal Q, Rozi M, Younis S, Mahar J (2017) Design and synthesis of 2, 6-di (substituted phenyl) thiazolo [3, 2-b]-1, 2, 4-triazoles as α-glucosidase and α-amylase inhibitors, co-relative pharmacokinetics and 3D QSAR and risk analysis. Biomed Pharmacother 94:499–513. https://doi.org/10.1016/j.biopha.2017.07.139

    CAS  Article  PubMed  Google Scholar 

  11. 11.

    Ali F, Khan KM, Salar U, Taha M, Ismail NH, Wadood A, Riaz M, Perveen S (2017) Hydrazinyl arylthiazole based pyridine scaffolds: synthesis, structural characterization, in vitro α-glucosidase inhibitory activity, and in silico studies. Eur J Med Chem 138:255–272. https://doi.org/10.1016/j.ejmech.2017.06.041

    CAS  Article  PubMed  Google Scholar 

  12. 12.

    Ghaslani D, Gorji ZE, Gorji AE, Riahi S (2017) Descriptive and predictive models for Henry’s law constant of CO2 in ionic liquids: a QSPR study. Chem Eng Res Des 120:15–25. https://doi.org/10.1016/j.cherd.2016.12.020

    CAS  Article  Google Scholar 

  13. 13.

    Hasanebrahimi G, Riahi S, Fini MF (2017) Exploring beneficial structural features of ionic surfactants for wettability alteration of carbonate rocks using QSPR modeling technique. J Mol Liq 240:196–208. https://doi.org/10.1016/j.molliq.2017.05.009

    CAS  Article  Google Scholar 

  14. 14.

    Mehraein I, Riahi S (2017) The QSPR models to predict the solubility of CO2 in ionic liquids based on least-squares support vector machines and genetic algorithm-multi linear regression. J Mol Liq 225:521–530. https://doi.org/10.1016/j.molliq.2016.10.133

    CAS  Article  Google Scholar 

  15. 15.

    Abbasi-Radmoghaddam Z, Riahi S, Gharaghani S, Mohammadi-Khanaposhtanai M (2020) Design of potential anti-tumor PARP-1 inhibitors by QSAR and molecular modeling studies. Mol Divers. https://doi.org/10.1007/s11030-020-10063-9

    Article  PubMed  Google Scholar 

  16. 16.

    Rezaei B, Riahi S (2016) Prediction of CO2 loading of amines in carbon capture process using membrane contactors: a molecular modeling. J Nat Gas Sci Eng 33:388–396. https://doi.org/10.1016/j.jngse.2016.05.003

    CAS  Article  Google Scholar 

  17. 17.

    Liu Z, Liu Y, Zeng G, Shao B, Chen M, Li Z, Jiang Y, Liu Y, Zhang Y, Zhong H (2018) Application of molecular docking for the degradation of organic pollutants in the environmental remediation: a review. Chemosphere 203:139–150. https://doi.org/10.1016/j.chemosphere.2018.03.179

    CAS  Article  PubMed  Google Scholar 

  18. 18.

    Liu Y, Ke Z, Cui J, Chen WH, Ma L, Wang B (2008) Synthesis, inhibitory activities, and QSAR study of xanthone derivatives as α-glucosidase inhibitors. Bioorg Med Chem 16(15):7185–7192. https://doi.org/10.1016/j.bmc.2008.06.043

    CAS  Article  PubMed  Google Scholar 

  19. 19.

    Kraim K, Khatmi D, Saihi Y, Ferkous F, Brahimi M (2009) Quantitative structure activity relationship for the computational prediction of α-glucosidase inhibitory. Chemom Intell Lab Syst 97(2):118–126. https://doi.org/10.1016/j.chemolab.2009.03.006

    CAS  Article  Google Scholar 

  20. 20.

    Release H (2002) 7.5 for windows, molecular modeling system, Hypercube. Inc. http://www.hyper.com

  21. 21.

    Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Mennucci B, Petersson GA, Nakatsuji H (2009) Gaussian 09, Gaussian, Inc., Wallingford, CT, vol 32, pp 5648–5652

  22. 22.

    Todeschini R, Consonni V, Mauri A, Pavan M (2002) DRAGON software. Milano, Italy

  23. 23.

    Gagic Z, Nikolic K, Ivkovic B, Filipic S, Agbaba D (2016) QSAR studies and design of new analogs of vitamin E with enhanced antiproliferative activity on MCF-7 breast cancer cells. J Taiwan Inst Chem Eng 59:33–44. https://doi.org/10.1016/j.jtice.2015.07.019

    CAS  Article  Google Scholar 

  24. 24.

    Roy K, Kar S, Das RN (2015) Understanding the basics of QSAR for applications in pharmaceutical sciences and risk assessment. Academic Press, London

    Google Scholar 

  25. 25.

    Golbraikh A, Tropsha A (2000) Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection. Mol Divers 5(4):231–243. https://doi.org/10.1023/A:1021372108686

    CAS  Article  Google Scholar 

  26. 26.

    Hawkins DM, Basak SC, Mills D (2003) Assessing model fit by cross-validation. J Chem Inf Comput Sci 43(2):579–586. https://doi.org/10.1021/ci025626i

    CAS  Article  PubMed  Google Scholar 

  27. 27.

    Gharaghani S, Khayamian T, Ebrahimi M (2013) Molecular dynamics simulation study and molecular docking descriptors in structure-based QSAR on acetylcholinesterase (AChE) inhibitors. SAR QSAR Environ Res 24(9):773–794. https://doi.org/10.1080/1062936X.2013.792877

    CAS  Article  PubMed  Google Scholar 

  28. 28.

    Aouidate A, Ghaleb A, Ghamali M, Chtita S, Choukrad M, Sbai A, Bouachrine M, Lakhlifi T (2016) Combining DFT and QSAR studies for predicting psychotomimetic activity of substituted phenethylamines using statistical methods. J Taibah Univers Sci 10(6):787–796. https://doi.org/10.1016/j.jtusci.2016.07.001

    Article  Google Scholar 

  29. 29.

    Pitman MR, Menz RI (2006). Methods for protein homology modelling. In: Applied mycology and biotechnology, vol 6. Elsevier, pp 37–59

  30. 30.

    https://www.uniprot.org/uniprot/P53341.fasta. Accessed Sept 2018

  31. 31.

    Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455–461. https://doi.org/10.1016/S1874-5334(06)80005-5

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Laskowski RA, Swindells MB. (2011). LigPlot+: multiple ligand–protein interaction diagrams for drug discovery, pp 2778–2786. https://doi.org/10.1021/ci200227u

  33. 33.

    He J, Peng T, Yang X, Liu H (2018) Development of QSAR models for predicting the binding affinity of endocrine disrupting chemicals to eight fish estrogen receptor. Ecotoxicol Environ Saf 148:211–219. https://doi.org/10.1016/j.ecoenv.2017.10.023

    CAS  Article  PubMed  Google Scholar 

  34. 34.

    Jouyban A, Shayanfar A, Ghafourian T, Acree WE Jr (2014) Solubility prediction of pharmaceuticals in dioxane + water mixtures at various temperatures: effects of different descriptors and feature selection methods. J Mol Liq 195:125–131. https://doi.org/10.1016/j.molliq.2014.02.012

    CAS  Article  Google Scholar 

  35. 35.

    Jukić M, Rastija V, Opačak-Bernardi T, Stolić I, Krstulović L, Bajić M, Glavaš-Obrovac L (2017) Antitumor activity of 3, 4-ethylenedioxythiophene derivatives and quantitative structure-activity relationship analysis. J Mol Struct 1133:66–73. https://doi.org/10.1016/j.molstruc.2016.11.074

    CAS  Article  Google Scholar 

  36. 36.

    Roy K, Das RN (2013) QSTR with extended topochemical atom (ETA) indices. 16. Development of predictive classification and regression models for toxicity of ionic liquids towards Daphnia magna. J Hazard Mater 254:166–178. https://doi.org/10.1016/j.jhazmat.2013.03.023

    CAS  Article  PubMed  Google Scholar 

  37. 37.

    Durrant JD, McCammon JA (2011) BINANA: a novel algorithm for ligand-binding characterization. J Mol Gr Model 29(6):888–893. https://doi.org/10.1016/j.jmgm.2011.01.004

    CAS  Article  Google Scholar 

Download references


The authors would like to gratefully acknowledge the support from the Institute of Petroleum Engineering (IPE), University of Tehran.

Author information



Corresponding author

Correspondence to Siavash Riahi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the supplementary material.

Supplementary material 1 (DOCX 82 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Izadpanah, E., Riahi, S., Abbasi-Radmoghaddam, Z. et al. A simple and robust model to predict the inhibitory activity of α-glucosidase inhibitors through combined QSAR modeling and molecular docking techniques. Mol Divers (2021). https://doi.org/10.1007/s11030-020-10164-5

Download citation


  • QSAR
  • Inhibitory activity
  • Docking
  • α-Glucosidase
  • Diabetes
  • Medicinal chemistry