Abstract
This paper discusses the experience of applying artificial intelligence methods for modeling metabolic networks in systems biology. Based on data from the KEGG database, a network of interactions between key metabolites and enzymes of human metabolism was constructed and analyzed using the Python NetworkX library. Topological properties of the network nodes and edges – indices of degree, intermediate and centrality of proximity – were calculated, and the role of essential and non-essential metabolites in the network was quantified. Statistical analysis confirmed the biological validity of the model and showed that essential metabolites play the role of central nodes. This study provides effective methods for a comprehensive and systematic study of metabolic processes at the interface of artificial intelligence and systems biology and opens up the possibility of predicting dynamic changes in in metabolism using graph neural networks.