Predictive QSAR modeling for the antioxidant activity of natural compounds derivatives based on Monte Carlo method

  • Shahin AhmadiEmail author
  • Hosein Ghanbari
  • Shahram Lotfi
  • Neda Azimi
Original Article


In this research, QSAR modeling was carried out through SMILES of compounds and on the basis of the Monte Carlo method to predict the antioxidant activity of 79 derivatives of pulvinic acid, 23 of coumarine, as well as nine structurally non-related compounds against three radiation sources of Fenton, gamma, and UV. QSAR model was designed through CORAL software, as well as a newer optimizing method well known as the index of ideality correlation. The full set of antioxidant compounds were randomly distributed into four sets, including training, invisible training, validation, and calibration; this division was repeated three times randomly. The optimal descriptors were picked up from a hybrid model by the combination of the hydrogen-suppressed graph and SMILES descriptors based on the objective function. These models’ predictability was assessed on the sets of validation. The results of three randomized sets showed that simple, robust, reliable, and predictive models were achieved for training, invisible training, validation, and calibration sets of all three models. The central decrease/increase descriptors were identified. This simple QSAR can be useful to predict antioxidant activity of numerous antioxidants.

Graphic abstract


QSAR Antioxidant activity CORAL software Monte Carlo 



The authors are thankful to Dr. Alla P. Toropova and Dr. Andrey A. Toropov for providing the CORAL software.

Compliance with ethical standards

Conflict of interest

The authors declare no conflicts of interest.

Supplementary material

11030_2019_10026_MOESM1_ESM.docx (56 kb)
Supplementary material 1 (DOCX 55 kb)


  1. 1.
    Lü JM, Lin PH, Yao Q, Chen C (2010) Chemical and molecular mechanisms of antioxidants: experimental approaches and model systems. J Cell Mol Med 14(4):840–860CrossRefGoogle Scholar
  2. 2.
    Lee A, Mercader AG, Duchowicz PR, Castro EA, Pomilio AB (2012) QSAR study of the DPPH radical scavenging activity of di (hetero) arylamines derivatives of benzo [b] thiophenes, halophenols and caffeic acid analogues. Chemometr Intell Lab Syst 116:33–40CrossRefGoogle Scholar
  3. 3.
    Valko M, Leibfritz D, Moncol J, Cronin MT, Mazur M, Telser J (2007) Free radicals and antioxidants in normal physiological functions and human disease. Int J Biochem Cell Biol 39(1):44–84CrossRefGoogle Scholar
  4. 4.
    Ahmadi S, Mehrabi M, Rezaei S, Mardafkan N (2019) Structure-activity relationship of the radical scavenging activities of some natural antioxidants based on the graph of atomic orbitals. J Mol Struct 1191:165–174CrossRefGoogle Scholar
  5. 5.
    Brewer M (2011) Natural antioxidants: sources, compounds, mechanisms of action, and potential applications. Compr Rev Food Sci Food Saf 10(4):221–247CrossRefGoogle Scholar
  6. 6.
    Habrant D, Poigny S, Ségur-Derai M, Brunel Y, Heurtaux BT, Le Gall T, Strehle A, Saladin R, Meunier S, Mioskowski C (2009) Evaluation of antioxidant properties of monoaromatic derivatives of pulvinic acids. J Med Chem 52(8):2454–2464CrossRefGoogle Scholar
  7. 7.
    Fusi J, Bianchi S, Daniele S, Pellegrini S, Martini C, Galetta F, Giovannini L, Franzoni F (2018) An in vitro comparative study of the antioxidant activity and SIRT1 modulation of natural compounds. Biomed Pharmacother 101:805–819CrossRefGoogle Scholar
  8. 8.
    Jeremić S, Radenković S, Filipović M, Antić M, Amić A, Marković Z (2017) Importance of hydrogen bonding and aromaticity indices in QSAR modeling of the antioxidative capacity of selected (poly) phenolic antioxidants. J Mol Graph Model 72:240–245CrossRefGoogle Scholar
  9. 9.
    Kostova I (2005) Synthetic and natural coumarins as cytotoxic agents. Curr Med Chem-Anti-Cancer Agents 5(1):29–46CrossRefGoogle Scholar
  10. 10.
    Bourdreux Y, Bodio E, Willis C, Billaud C, Le Gall T, Mioskowski C (2008) Synthesis of vulpinic and pulvinic acids from tetronic acid. Tetrahedron 64(37):8930–8937CrossRefGoogle Scholar
  11. 11.
    Benedict R, Brady L (1972) Antimicrobial activity of mushroom metabolites. J Pharm Sci 61(11):1820–1822CrossRefGoogle Scholar
  12. 12.
    Dias D, White J, Urban S (2007) Pinastric acid revisited: a complete NMR and X-ray structure assignment. Nat Prod Res 21(4):366–376CrossRefGoogle Scholar
  13. 13.
    Osman H, Arshad A, Lam CK, Bagley MC (2012) Microwave-assisted synthesis and antioxidant properties of hydrazinyl thiazolyl coumarin derivatives. Chem Cent J 6(1):32CrossRefGoogle Scholar
  14. 14.
    Hosseinimehr SJ (2007) Trends in the development of radioprotective agents. Drug Discover Today 12(19–20):794–805CrossRefGoogle Scholar
  15. 15.
    Weiss JF, Landauer MR (2009) History and development of radiation-protective agents. Int J Radiat Biol 85(7):539–573CrossRefGoogle Scholar
  16. 16.
    Le Roux A, Meunier S, Le Gall T, Denis JM, Bischoff P, Wagner A (2011) Synthesis and radioprotective properties of pulvinic acid derivatives. Chem Med Chem 6(3):561–569CrossRefGoogle Scholar
  17. 17.
    Okunieff P, Swarts S, Keng P, Sun W, Wang W, Kim J, Yang S, Zhang H, Liu C, Williams JP (2008) Antioxidants reduce consequences of radiation exposure. Oxygen Transport to Tissue XXIX. Springer, Boston, pp 165–178CrossRefGoogle Scholar
  18. 18.
    Le Roux A, Kuzmanovski I, Habrant D, Meunier S, Bischoff P, Nadal B, Thetiot-Laurent SA-L, Le Gall T, Wagner A, Novic M (2011) Design and synthesis of new antioxidants predicted by the model developed on a set of pulvinic acid derivatives. J Chem Inf Model 51(12):3050–3059CrossRefGoogle Scholar
  19. 19.
    Ahmadi S, Khani R, Moghaddas M (2018) Prediction of anti-cancer activity of 1, 8-naphthyridin derivatives by using of genetic algorithm-stepwise multiple linear regression. Med Sci J Islam Azad Univ-Tehran Med Branch 28(3):181–194Google Scholar
  20. 20.
    Ahmadi S, Khazaei MR, Abdolmaleki A (2014) Quantitative structure–property relationship study on the intercalation of anticancer drugs with ct-DNA. Med Chem Res 23(3):1148–1161CrossRefGoogle Scholar
  21. 21.
    Ahmadi S (2012) A QSPR study of association constants of macrocycles toward sodium cation. Macroheterocycles 5(1):23–31CrossRefGoogle Scholar
  22. 22.
    Habibpour E, Ahmadi S (2017) QSAR modeling of the arylthioindole class of colchicine polymerization inhibitors as anticancer agents. Curr Comput Aided Drug Des 13(2):143–159CrossRefGoogle Scholar
  23. 23.
    Ahmadi S, Habibpour E (2017) Application of GA-MLR for QSAR modeling of the arylthioindole class of tubulin polymerization inhibitors as anticancer agents. Anti-Cancer Agents Med Chem (Formerly Curr Med Chemistry-Anti-Cancer Agents) 17(4):552–565Google Scholar
  24. 24.
    Ahmadi S, Ganji S (2016) Genetic algorithm and self-organizing maps for QSPR study of some N-aryl derivatives as butyrylcholinesterase inhibitors. Curr Drug Discov Technol 13(4):232–253CrossRefGoogle Scholar
  25. 25.
    Ahmadi S, Babaee E (2014) 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 79(1–2):141–149CrossRefGoogle Scholar
  26. 26.
    Ahmadi S (2012) 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 74(1–4):57–66CrossRefGoogle Scholar
  27. 27.
    Ghasemi JB, Ahmadi S, Ayati M (2010) QSPR modeling of stability constants of the Li-hemispherands complexes using MLR: a theoretical host-guest study. Macroheterocycles 3(4):234–242CrossRefGoogle Scholar
  28. 28.
    Ghasemi JB, Zohrabi P, Khajehsharifi H (2010) Quantitative structure–activity relationship study of nonpeptide antagonists of CXCR2 using stepwise multiple linear regression analysis. Monatshefte Chemie-Chemical Monthly 141(1):111–118CrossRefGoogle Scholar
  29. 29.
    Ghasemi J, Ahmadi S (2007) Combination of genetic algorithm and partial least squares for cloud point prediction of nonionic surfactants from molecular structures. Annali di Chimica J Anal, Environ Cultural Herit Chem 97(1–2):69–83CrossRefGoogle Scholar
  30. 30.
    Ghasemi JB, Ahmadi S, Brown S (2011) A quantitative structure–retention relationship study for prediction of chromatographic relative retention time of chlorinated monoterpenes. Environ Chem Lett 9(1):87–96CrossRefGoogle Scholar
  31. 31.
    Kuzmanovski I, Wagner A, Novič M (2015) Development of models for prediction of the antioxidant activity of derivatives of natural compounds. Anal Chim Acta 868:23–35CrossRefGoogle Scholar
  32. 32.
    Goya Jorge E, Rayar A, Barigye S, Jorge Rodríguez M, Sylla-Iyarreta Veitía M (2016) Development of an in silico model of DPPH free radical scavenging capacity prediction of antioxidant activity of coumarin type compounds. Int J Mol Sci 17(6):881CrossRefGoogle Scholar
  33. 33.
    Alisi IO, Uzairu A, Abechi SE, Idris SO (2018) Evaluation of the antioxidant properties of curcumin derivatives by genetic function algorithm. J Adv Res 12:47–54CrossRefGoogle Scholar
  34. 34.
    Ahmadi S, Mardinia F, Azimi N, Qomi M, Balali E (2019) Prediction of chalcone derivative cytotoxicity activity against MCF-7 human breast cancer cell by Monte Carlo method. J Mol Struct 1181:305–311CrossRefGoogle Scholar
  35. 35.
    Ahmadi S, Akbari A (2018) Prediction of the adsorption coefficients of some aromatic compounds on multi-wall carbon nanotubes by the Monte Carlo method. SAR QSAR Environ Res 29(11):895–909CrossRefGoogle Scholar
  36. 36.
    Toropova AP, Toropov AA (2019) Quasi-SMILES: quantitative structure–activity relationships to predict anticancer activity. Mol Diversity 23(2):403–412CrossRefGoogle Scholar
  37. 37.
    Ahmadi S (2019) Mathematical modeling of cytotoxicity of metal oxide nanoparticles using the index of ideality correlation criteria. Chemosphere 242:125192CrossRefGoogle Scholar
  38. 38.
    Kumar P, Kumar A, Sindhu J (2019) Design and development of novel focal adhesion kinase (FAK) inhibitors using Monte Carlo method with index of ideality of correlation to validate QSAR. SAR QSAR Environ Res 30(2):63–80CrossRefGoogle Scholar
  39. 39.
    Toropov AA, Toropova AP, Cappellini L, Benfenati E, Davoli E (2018) QSPR analysis of threshold of odor for the large number of heterogenic chemicals. Mol Divers 22(2):397–403CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Shahin Ahmadi
    • 1
    Email author
  • Hosein Ghanbari
    • 1
  • Shahram Lotfi
    • 2
  • Neda Azimi
    • 3
  1. 1.Department of ChemistryKermanshah Branch, Islamic Azad UniversityKermanshahIran
  2. 2.Department of ChemistryPayame Noor University (PNU)TehranIran
  3. 3.Department of Chemical EngineeringKermanshah Branch, Islamic Azad UniversityKermanshahIran

Personalised recommendations