NEURAL NETWORK MODELING OF MULTI-TARGET SEROTONERGIC MECHANISM OF CELLULAR STRESS
DOI: 10.22138/2500-0918-2025-22-1-73-91
P.M. Vassiliev¹,², A.P. Sarapultsev¹,³, M.V. Komelkova¹, M.A. Perfilev¹,²,
A.N. Kochetkov², G.P. Sarapultsev³, S.A. Fedorov¹, P.O. Platkovski1,
I.A. Utepova1,⁴, S.L. Deev⁴
¹ Federal State Autonomous Educational Institution of Higher Education «South Ural State University (national research university)», Chelyabinsk, Russian Federation;
² Federal State Budgetary Educational Institution of Higher Education «Volgograd State Medical University» of the Ministry of Health of the Russian Federation, Volgograd, Russian Federation;
³ Federal State Budgetary Scientific Institution Institute of Immunology and Physiology of the Ural Branch of the Russian Academy of Sciences, Ekaterinburg, Russian Federation;
⁴ Federal State Autonomous Educational Institution of Higher Education «Ural Federal University named after the first President of Russia B.N.Yeltsin», Ekaterinburg, Russian Federation
Abstract. Aim. Construction of a neural network multi-target regression model of the dependence of the SERT affinity of chemical compounds on the energies of their docking into relevant biotargets, sensitivity analysis of the resulting model and identification of key biotargets that most significantly affect the affinity of compounds to the serotonin transporter. Materials and Methods. Data on 2D structure and SERT inhibitory activity of 3436 known compounds and drugs; 478 3D models of human target proteins relevant to SERT inhibitory activity; 10 original and licensed computer programs. The following were performed: construction of optimized 3D models of SERT inhibitory compounds and drugs; identification of biotargets relevant to SERT inhibitory activity; finding binding sites and valid 3D models of these biotargets; ensemble docking of known SERT inhibitors to relevant biotargets, calculation of minimum docking energies, formation of a training set; training and testing of regression neural networks, selection of the best neural network; sensitivity analysis of the neural network multi-target model, identification of key biotargets that significantly affect SERT affinity. Results. 21 biotargets relevant to SERT inhibitory activity were identified, 25 binding sites were revealed for them, 68 valid 3D models were found. Ensemble docking of known SERT inhibitors into relevant biotargets was performed, a training set was formed. 7500 neural networks were trained, a regression neural network model of the dependence of SERT affinity of compounds on their affinity to 24 sites of 20 relevant biotargets was built, having a correlation coefficient RTotal=0.885 and statistical significance p<5·10ˉ⁷. A sensitivity analysis of this model was performed, 12 key biotargets were identified that most significantly affect SERT affinity. Conclusions. Using artificial intelligence methods, 12 key biotargets were found that most significantly affect the affinity to SERT, of which six targets are characterized by a positive effect (ADRA1A, AMPA, DRD1, GAT1, HTR2A Alo, HTR3A) and six targets are characterized by a negative effect (HTR2C, JAK2, MAPK11, NMDA Block, ROCK2, SLC18A2). These biotargets reflect the multitarget mechanism of serotonergic regulation of cellular stress in inflammation and various functional disorders.
Keywords: artificial neural networks, serotonin transporter inhibitors, relevant biotargets, docking, serotonergic mechanisms of cellular stress
There is no conflict of interest.
Contact details of the corresponding author:
Alexey P. Sarapultsev
a.sarapultsev@gmail.com
Received: 28.11.2024
For citation: Vassiliev P.M., Sarapultsev A.P., Komelkova M.V., Perfilev M.A., Kochetkov A.N., Sarapultsev G.P., Fedorov S.A., Platkovski P.O., Utepova I.A., Deev S.L. Neural network modeling of multi-target serotonergic mechanism of cellular stress. [Online] Vestn. Ural. Med. Akad. Nauki. = Journal of Ural Medical Academic Science. 2025, Vol. 22, no. 1, pp. 73–91. DOI: 10.22138/2500-0918-2025-22-1-73-91 (In Russ)
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Authors
Pavel M. Vassiliev
Federal State Budgetary Educational Institution of Higher Education «Volgograd State Medical University» of the Ministry of Health of the Russian Federation
Doctor of Biological Sciences, senior researcher at the Higher Attestation Commission (Associate Professor)
Head of Laboratory for Information Technology in Pharmacology and Computer Modeling of Drugs for Research Center of Innovative Medicines with Pilot Industrial Production
pvassilev@mail.ru
Volgograd, Russian Federation
Alexey P. Sarapultsev
Institute of Immunology and Physiology of the Ural Branch of the Russian Academy of Sciences
Doctor of Biological Sciences
Head of Laboratory of Immunopathophysiology of IIP UB RAS
a.sarapultsev@gmail.com
Yekaterinburg, Russian Federation
Mariia V. Komelkova
Federal State Autonomous Educational Institution of Higher Education «South Ural State University (national research university)»
Doctor of Biological Sciences
Senior Researcher of Russian-Chinese Education and Research Center of System Pathology
mkomelkova@mail.ru
Chelyabinsk, Russian Federation
Maksim A. Perfilev
Federal State Budgetary Educational Institution of Higher Education «Volgograd State Medical University» of the Ministry of Health of the Russian Federation
Junior researcher of Laboratory for Information Technology in Pharmacology and Computer Modeling of Drugs for Research Center of Innovative Medicines with Pilot Industrial Production
maxim.firu@yandex.com
Volgograd, Russian Federation
Andrey N. Kochetkov
Federal State Budgetary Educational Institution of Higher Education «Volgograd State Medical University» of the Ministry of Health of the Russian Federation
System administrator of Laboratory for Information Technology in Pharmacology and Computer Modeling of Drugs for Research Center of Innovative Medicines with Pilot Industrial Production
akocha@mail.ru
Volgograd, Russian Federation
German P. Sarapultsev
Institute of Immunology and Physiology of the Ural Branch of the Russian Academy of Sciences
Graduate student
dr.sarapultsev@gmail.com
Yekaterinburg, Russian Federation
Stanislav A. Fedorov
Federal State Autonomous Educational Institution of Higher Education «South Ural State University (national research university)»
Laboratory research assistant at the Department of Scientific and Innovative Activities
fedorovstas2016@yandex.ru
Chelyabinsk, Russian Federation
Pavel O. Platkovski
Federal State Autonomous Educational Institution of Higher Education «South Ural State University (national research university)»
Laboratory research assistant at the Department of Scientific and Innovative Activities
paw.platkovski@yandex.ru
Chelyabinsk, Russian Federation
Irina A. Utepova
Federal State Autonomous Educational Institution of Higher Education «Ural Federal University named after the first President of Russia B.N.Yeltsin»
Doctor of Chemical Sciences, Associate Professor
Professor
irina-utepova@yandex.ru
Yekaterinburg, Russian Federation
Sergey L. Deev
Federal State Autonomous Educational Institution of Higher Education «Ural Federal University named after the first President of Russia B.N.Yeltsin»
PhD, Associate Professor
Associate Professor
deevsl@yandex.ru
Yekaterinburg, Russian Federation