Neural Network Modeling of the Multitarget Rage Inhibitory Activity

  • P. M. VassilievEmail author
  • A. A. Spasov
  • L. R. Yanaliyeva
  • A. N. Kochetkov
  • V. V. Vorfolomeyeva
  • V. G. Klochkov
  • D. T. Appazova


The models describing the dependence of the level of RAGE-inhibitory activity on the affinity of compounds for target proteins of the RAGE—NF-κB signaling pathway have been constructed by means of the artificial neural networks methodology. For this purpose, a validated database on the structures and activity levels of 183 compounds available in the literature and tested for RAGE inhibitory activity was created. The analysis of AGE—RAGE signaling pathways resulted in detection of 14 key RAGE–NF-κB signal pathway nodes, for which 34 relevant target proteins were identified. A database of 66 valid 3D models of 22 target proteins of the RAGE—NF-κB signal chain was formed. Ensemble molecular docking of 3D models of 183 known RAGE inhibitors into sites of 66 valid 3D models of 22 relevant RAGE target proteins was performed and minimum docking energies for each compound were determined for each target. According to the method of artificial multilayer perceptron neural networks, classification models were constructed to predict the level of RAGE inhibitory activity by the calculated affinity of compounds for significant target proteins of the RAGE–NF-κB signaling chain. The prognostic ability of these models of RAGE-inhibitory activity was evaluated; the maximum accuracy according to ROC analysis was 90% for a high level of activity. The performed analysis of sensitivity of the developed multitarget models revealed most valuable targets of the RAGE–NF-κB signal transduction pathway. It was found that for high level of RAGE inhibitory activity, the most significant biotargets included not only AGE receptors, but eight signaling kinases of the RAGE–NF-κB pathway and the transcription factor NF-κB1. It is suggested that known compounds with high RAGE-inhibitory activity are preferential inhibitors of signal kinases.


RAGE–NF-κB signaling pathway RAGE inhibitors multitarget affinity molecular docking artificial neural networks 



This work was financially supported by the Russian Foundation for Basic Research (project no. 18-015-00499).


This article does not contain any research involving humans or using animals as experimental objects.


  1. 1.
    Ansari, N.A. and Rashid, Z., Biomed. Khim., 2010, vol. 56, pp. 168–178.CrossRefGoogle Scholar
  2. 2.
    KEGG: AGE-RAGE signaling pathway in diabetic complications—Homo sapiens, hsa04933&show_description=show, 2017.Google Scholar
  3. 3.
    Tobon-Velasco, J.C., Cuevas, E., and Torres-Ramos, M.A., CNS Neurol. Disord. Drug Targets, 2014, vol. 13, pp. 1615–1626.CrossRefGoogle Scholar
  4. 4.
    Yan, S.F., Ramasamy, R., and Schmidt, A.M., Nat. Clin. Pract. Endocrinol. Metab., 2008, vol. 4, pp. 285–293.CrossRefGoogle Scholar
  5. 5.
    Singh, V.P., Bali, A., Singh, N., and Jaggi, A.S., Korean. J. Physiol. Pharmacol., 2014, vol. 18, pp. 1–14.CrossRefGoogle Scholar
  6. 6.
    Matrone, C., Djelloul, M., Taglialatela, G., and Perrone, L., Histol. Histopathol., 2015, vol. 30, pp. 125–139.Google Scholar
  7. 7.
    ChEMBL,, 2018.Google Scholar
  8. 8.
    BindingDB,, 2018.Google Scholar
  9. 9.
    PubChem,, 2018.Google Scholar
  10. 10.
    Statistica,, 2007.Google Scholar
  11. 11.
    Vassiliev, P.M., Yanalieva, L.R., Spasov, A.A., Kochetkov, A.N., Vorfolomeeva, V.V., and Klochkov, V.G., Ingibitory retseptorov konechnykh produktov glikirovaniya (Advanced Glycation End Product Receptor Inhibitors), Certificate of State Registration of the Database no. 2019620160. (24.01.2019), Offits. Bull. Programmy dlya EVM, baz dannykh i topologiy integral’nykh mikroskhem ETIN (Programs for Computers, Databases and Topologies of Integrated Circuits), 2, RU 2019620160.Google Scholar
  12. 12.
    UniProtKB,, 2018.Google Scholar
  13. 13.
    PubMed,, 2018.Google Scholar
  14. 14.
    Vassiliev, P.M., Yanalieva, L.R., Kochetkov, A.N., Vorfolomeeva, V.V., and Klochkov, V.G., Vestnik VolgGMU, 2018, vol. 3, pp. 133–138.Google Scholar
  15. 15.
    PDBe,, 2018.Google Scholar
  16. 16.
    ModBase,, 2018.Google Scholar
  17. 17.
    VMD,, 2016.Google Scholar
  18. 18.
    Laskowski, R.A. and Swindells, M.B., J. Chem. Inf. Model., 2011, vol. 51, pp. 2778–2786.CrossRefGoogle Scholar
  19. 19.
    Inte:ligand,, 2018.Google Scholar
  20. 20.
    IUPHAR/BPS, http://www.guidetopharmacology. org/, 2018.Google Scholar
  21. 21.
    ChemAxon: Marvin,, 2017.Google Scholar
  22. 22.
    MOPAC,, 2016.Google Scholar
  23. 23.
    Trott, O. and Olson, A.J., J. Comp. Chem., 2010, vol. 31, pp. 455–461.Google Scholar
  24. 24.
    Neironnye seti. Statistica Neural Networks: Metodologiya i tekhnologiya sovremennogo analiza dannykh (Neural networks. Statistica Neural Networks: Methodology and Technology of Modern Data Analysis), Moscow: Goryachaya liniya—Telekom, 2008.Google Scholar
  25. 25.
    Kolmogorov, A.N., Dokl. Akad. Nauk SSSR, 1958, vol. 114, pp. 953–956.Google Scholar
  26. 26.
    Yang, B. and Papoian, T., J. Appl. Toxicol., 2018, vol. 38, pp. 790–800.CrossRefGoogle Scholar
  27. 27.
    GUSAR AcuteRodentstoxicity, http://www.way2drug. com/gusar/acutoxpredict.html, 2019.Google Scholar
  28. 28.
    ProTox-II, compound_input, 2019.Google Scholar
  29. 29.
    Glotov, N.V., Zhivotovsky, L.A., Khovanov, N.V., and Khromov-Borisov, N.N., Biometriya (Biometrics), Leningrad: University Press, 1982.Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2019

Authors and Affiliations

  • P. M. Vassiliev
    • 1
    Email author
  • A. A. Spasov
    • 1
  • L. R. Yanaliyeva
    • 1
  • A. N. Kochetkov
    • 1
  • V. V. Vorfolomeyeva
    • 1
  • V. G. Klochkov
    • 1
  • D. T. Appazova
    • 1
  1. 1.Volgograd State Medical UniversityVolgogradRussia

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