187
Views
3
CrossRef citations to date
0
Altmetric
Research Articles

SMILES-based QSAR and molecular docking study of xanthone derivatives as α-glucosidase inhibitors

, , &
Pages 361-372 | Received 10 Jun 2021, Accepted 16 Jul 2021, Published online: 12 Aug 2021

References

  • Cho N, Shaw J, Karuranga S, et al. IDF diabetes atlas: global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract. 2018;138:271–281.
  • Forouhi NG, Wareham NJ. Epidemiology of diabetes. Medicine. 2019;47(1):22–27.
  • Kohei K. Pathophysiology of type 2 diabetes and its treatment policy. JMAJ. 2010;53(1):41–46.
  • Deswal L, Verma V, Kumar D, et al. Synthesis and antidiabetic evaluation of benzimidazole‐tethered 1, 2, 3‐triazoles. Arch Pharm. 2020;353(9):2000090.
  • Shen Q, Chitchumroonchokchai C, Thomas JL, et al. Adipocyte reporter assays: application for identification of anti-inflammatory and antioxidant properties of mangosteen xanthones. Mol Nutr Food Res. 2014;58(2):239–247.
  • Narasimhan S, Maheshwaran S, Abu-Yousef IA, et al. Anti-bacterial and anti-fungal activity of xanthones obtained via semi-synthetic modification of α-mangostin from Garcinia mangostana. Molecules. 2017;22(2):275.
  • Groweiss A, Cardellina JH, Boyd MR. HIV-inhibitory prenylated xanthones and flavones from Maclura tinctoria. J Nat Prod. 2000;63(11):1537–1539.
  • Wang Q, Ma C, Ma Y, et al. Structure-activity relationships of diverse xanthones against multidrug resistant human tumor cells. Bioorg Med Chem Lett. 2017;27(3):447–449.
  • Ding SM, Lan T, Ye GJ, et al. Novel oxazolxanthone derivatives as a new type of α-glucosidase inhibitor: synthesis, activities, inhibitory modes and synergetic effect. Bioorg Med Chem. 2018;26(12):3370–3378.
  • Liu Y, Ma L, Chen WH, et al. Binding mechanism and synergetic effects of xanthone derivatives as noncompetitive α-glucosidase inhibitors: a theoretical and experimental study. J Phys Chem B. 2013;117(43):13464–13471.
  • Li GL, Cai CY, He JY, et al. Synthesis of 3-acyloxyxanthone derivatives as α-glucosidase inhibitors: a further insight into the 3-substituents' effect. Bioorg Med Chem. 2016;24(7):1431–1438.
  • Liu Y, Ke Z, Cui J, et al. Synthesis, inhibitory activities, and QSAR study of xanthone derivatives as alpha-glucosidase inhibitors. Bioorg Med Chem. 2008;16(15):7185–7192.
  • Li GL, He JY, Zhang A, et al. Toward potent α-glucosidase inhibitors based on xanthones: a closer look into the structure-activity correlations. Eur J Med Chem. 2011;46(9):4050–4055.
  • Ahmadi S, Khazaei MR, Abdolmaleki A. Quantitative structure–property relationship study on the intercalation of anticancer drugs with ct-DNA. Med Chem Res. 2014;23(3):1148–1161.
  • Ghasemi JB, Ahmadi S, Brown S. A quantitative structure–retention relationship study for prediction of chromatographic relative retention time of chlorinated monoterpenes. Environ Chem Lett. 2011;9(1):87–96.
  • Ghasemi JB, Ahmadi S, Ayati M. QSPR modeling of stability constants of the Li-hemispherands complexes using MLR: a theoretical host-guest study. Macroheterocycles. 2010;3(4):234–242.
  • Javidfar M, Ahmadi S. QSAR modelling of larvicidal phytocompounds against Aedes aegypti using index of ideality of correlation. SAR QSAR Environ Res. 2020;31(10):717–739.
  • Ahmadi S, Lotfi S, Kumar P. A Monte Carlo method based QSPR model for prediction of reaction rate constants of hydrated electrons with organic contaminants. SAR QSAR Environ Res. 2020;31(12):935–950.
  • Ahmadi S. Mathematical modeling of cytotoxicity of metal oxide nanoparticles using the index of ideality correlation criteria. Chemosphere. 2020;242:125192.
  • Kumar P, Kumar A. CORAL: QSAR models of CB1 cannabinoid receptor inhibitors based on local and global SMILES attributes with the index of ideality of correlation and the correlation contradiction index. Chemometr Intelligent Lab Syst. 2020;200:103982.
  • Zheng X, Zhou S, Zhang C, et al. Docking-assisted 3D-QSAR studies on Xanthones as α-glucosidase inhibitors. J Mol Model. 2017;23(9):272.
  • Kumar P, Kumar A, Sindhu J. In silico design of diacylglycerol acyltransferase-1 (DGAT1) inhibitors based on SMILES descriptors using Monte-Carlo method. SAR QSAR Environ Res. 2019;30(8):525–541.
  • Toropov AA, Toropova AP. QSPR/QSAR: state-of-art, weirdness, the future. Molecules. 2020;25(6):1292.
  • Lotfi S, Ahmadi S, Zohrabi P. QSAR modeling of toxicities of ionic liquids toward Staphylococcus aureus using SMILES and graph invariants. Struct Chem. 2020;31(6):2257–2270.
  • Liu Y, Zou L, Ma L, et al. Synthesis and pharmacological activities of xanthone derivatives as alpha-glucosidase inhibitors. Bioorg Med Chem. 2006;14(16):5683–5690.
  • Li ZP, Song YH, Uddin Z, et al. Inhibition of protein tyrosine phosphatase 1B (PTP1B) and α-glucosidase by xanthones from Cratoxylum cochinchinense, and their kinetic characterization. Bioorg Med Chem. 2018;26(3):737–746.
  • Ye GJ, Lan T, Huang ZX, et al. Design and synthesis of novel xanthone-triazole derivatives as potential antidiabetic agents: α-Glucosidase inhibition and glucose uptake promotion. Eur J Med Chem. 2019;177:362–373.
  • Lotfi S, Ahmadi S, Kumar P. A Hybrid Descriptor based QSPR model to predict the thermal decomposition temperature of imidazolium ionic liquids using Monte Carlo approach. J Mol Liq. 2021;338:116465.
  • Toropov AA, Toropova AP. Predicting cytotoxicity of 2-phenylindole derivatives against breast cancer cells using index of ideality of correlation. Anticancer Res. 2018;38(11):6189–6194.
  • Ahmadi S, Babaee E, Khazaei MR. Application of self organizing maps and GA-MLR for the estimation of stability constant of 18-crown-6 ether derivatives with sodium cation. J Incl Phenom Macrocycl Chem. 2014;79(1–2):141–149.
  • Ahmadi S, Habibpour E. Application of GA-MLR for QSAR modeling of the arylthioindole class of tubulin polymerization inhibitors as anticancer agents. Anticancer Agents Med Chem. 2017;17(4):552–565.
  • Ahmadi S. Application of GA-MLR method in QSPR modeling of stability constants of diverse 15-crown-5 complexes with sodium cation. J Incl Phenom Macrocycl Chem. 2012;74(1–4):57–66.
  • Golbraikh A, Tropsha A. Beware of q2!. J Mol Graph Model. 2002;20(4):269–276.
  • Schüürmann G, Ebert RU, Chen J, et al. External validation and prediction employing the predictive squared correlation coefficient test set activity mean vs. training set activity mean. J Chem Inf Model. 2008;48(11):2140–2145.
  • Pratim Roy P, Paul S, Mitra I, et al. On two novel parameters for validation of predictive QSAR models. Molecules. 2009;14(5):1660–1701.
  • Roy K, Chakraborty P, Mitra I, et al. Some case studies on application of “ “r(m)2” metrics for judging quality of quantitative structure-activity relationship predictions: emphasis on scaling of response data”. J Comput Chem. 2013;34(12):1071–1082.
  • Gramatica P, Sangion A. A historical excursus on the statistical validation parameters for QSAR models: a clarification concerning metrics and terminology. J Chem Inf Model. 2016;56(6):1127–1131.
  • Chirico N, Gramatica P. Real external predictivity of QSAR models: how to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient. J Chem Inf Model. 2011;51(9):2320–2335.
  • Toropova AP, Toropov AA, Leszczynska D, et al. The index of ideality of correlation: models of the flash points of ternary mixtures. New J Chem. 2020;44(12):4858–4868.
  • Kumar A, Kumar P. Construction of pioneering quantitative structure activity relationship screening models for abuse potential of designer drugs using index of ideality of correlation in monte carlo optimization. Arch Toxicol. 2020;94(9):3069–3086.
  • Ahmadi S, Mardinia F, Azimi N, et al. Prediction of chalcone derivative cytotoxicity activity against MCF-7 human breast cancer cell by Monte Carlo method. J Mol Struct. 2019;1181:305–311.
  • Yamamoto K, Miyake H, Kusunoki M, et al. Crystal structures of isomaltase from Saccharomyces cerevisiae and in complex with its competitive inhibitor maltose. FEBS J. 2010;277(20):4205–4214.
  • Comelli NC, Ortiz EV, Kolacz M, et al. Conformation-independent QSAR on c-Src tyrosine kinase inhibitors. Chemometr Intelligent Lab Syst. 2014;134:47–52.
  • Malik S, Khan A, Naseer MM, et al. Z. Shafiq, Xanthenone-based hydrazones as potent α-glucosidase inhibitors: synthesis, solid state self-assembly and in silico studies. Bioorg Chem. 2019;84:372–383.
  • Wallace AC, Laskowski RA, Thornton JM. LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. Protein Eng. 1995;8(2):127–134.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.