Authors
Petrenko P. B.
Doctor of Technical Sciences, Professor, Deputy Head of the Algorithmic Solutions Department Synergy Design Bureau, Signal Processing Center, 180 Ligovsky Ave., Saint Petersburg, Russia
Corresponding author
Petrenko Pavel Borisovich; e-mail: prof.petrenko54@gmail.com
Funding
The study had no sponsorship support.
Conflict of interest
The author declares no conflict of interest.
Received
02.02.2026
Accepted for publication
07.04.2026
Abstract
The review presents modern achievements in the application of graph neural networks to solve urgent problems in bioinformatics and medicine. The article focuses on the fundamental reasons why graph neural networks should be used to analyze biological and medical data, and the basic principles of their application. The theory of creating graph neural networks is in the trend of artificial intelligence development and provides great prospects for realizing the advantages of machine learning in practice. The effectiveness of their use is due to the ability to generalize heterogeneous information, resistance to incomplete, fuzzy and noisy data; the ability to work with large amounts of information, including graph structures; good adaptation of the models used and compatibility with modern methods of parallel computing. In this regard, progressive achievements have been achieved in biomedical research, traffic forecasting, genomics, applied to knowledge graphs and in other applications. Examples of effective use of graph neural networks in bioinformatics and medicine are given, and future research directions are outlined. It has been shown that the use of GNN significantly increases the accuracy of diagnosis, accelerates the creation and testing of new drugs, and raises the level of interaction between the use of advanced computer technologies and patient treatment.
Key words
graph neural networks (GNN), graph representation training, deep learning on GNN, medical visualization and interpretability of data, recommendation systems for safe and effective medicines, prediction of properties of molecules and protein structure
DOI
