Protein-RNA interactions are involved in many biological processes. Modeling and studying protein and RNA molecules can help us understand the workings of proteins and RNA. In this master's thesis, we developed a procedure for predicting protein-RNA interactions on a protein using convolutional neural networks over graphs. We obtained the data from the PDB database, preprocessed it into a graph structure, and added appropriate features to each atom. Thus, the data are suitable for graph neural network models. We analyzed the models and presented the results with different performance metrics. The best model achieved good results (ROC AUC = 0.9). We also implemented a graphical interface to visualize the structure of proteins and the predicted sites of interaction with RNA in 3D space.
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