Understanding cell–cell interactions is crucial for unraveling the complexities of multicellular organisms and holds promising implications for advancements in medical science. These interactions, mediated through specific ligand-receptor pairs, remain partially identified. The rapid evolution of gene expression analysis technologies, especially spatial transcriptomics, now allows for the precise capture of gene expression while maintaining cellular localization. While studies using spatial transcriptomics data to visualize known cell–cell interactions are achieving great success, their application to infer unknown cell–cell interaction pairs has not yet been fully investigated. In this study, we introduce a novel approach utilizing Graph Convolutional Neural Networks (GCNN) to infer cell–cell interactions from spatial transcriptomics data. Previous efforts have demonstrated the utility of GCNNs for data obtained through the continuous FISH (fluorescence in situ hybridization) method. We propose an alternative strategy to adapt GCNN-based cell–cell interaction prediction methods to data acquired by in situ capture methods. Additionally, we address the challenge of properly generating training data for the model, implementing a solution that significantly enhances the estimation process. Our findings reveal that the method used to transform Spatial Transcriptomics data into a graph significantly impacts the accuracy of interaction predictions, with prediction accuracies ranging from 80% to 90% under certain conditions.