DOI
10.34229/KCA2522-9664.25.1.5
UDC 519.7
1 Educational and Research Institute for Applied System Analysis of the National Technical University of Ukraine
“Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine
 mzz@kpi.ua
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2 Educational and Research Institute for Applied System Analysis of the National Technical University of Ukraine
“Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine
kasyanov@i.ua
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4 National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine
vlad.novykov@gmail.com
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AI METHODOLOGY FOR MODELING PROTEIN INTERACTIONS
IN BIOLOGICAL SYSTEMS
Abstract. This paper proposes a methodology for developing an artificial intelligence system for modeling protein interactions in biological systems based on reaction-diffusion equations with multivalued interaction functions. The primary goal of the research is to approximate the solutions of these equations using highly efficient computational methods, specifically physics-informed neural networks (PINN) and the deep learning Galerkin method (DLGM). The proposed system utilizes machine learning to model complex biological processes while accounting for real cellular conditions. The authors have developed and rigorously justified a computational algorithm that, on the current level of mathematical rigor, ensures the approximation of solutions to infinite-dimensional stochastic optimization problems and demonstrates superior efficiency compared to traditional methods. The high accuracy and speed of the obtained results enable extending this methodology to other types of partial differential equations, particularly for biological and medical applications.
Keywords: reaction-diffusion equation, multivariate interaction functions, machine learning, physical information neural network, approximation solution.
full text
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