<p>Protein–protein interactions (PPIs) are critical in various biological processes, such as signal transduction, immunological responses, and cellular pathways. Structural PPI prediction leverages computational models to predict interactions reliably using protein structures. Understanding comprehensive cancer PPIs is essential for uncovering the precise molecular processes that drive different diseases and advancing specific therapeutics. Nevertheless, precisely predicting cancer PPIs is still a challenge due to the intricate and dynamic nature of the proteins. The research presents a novel approach called CanGRNNA that integrates the graph structure and the temporal patterns of proteins using graph recurrent neural networks (GRNN) with attention for accurate prediction of cancer PPIs. Attention identifies the most pertinent residues and context of protein structures to enhance the precision of interaction predictions. The approach captures the structural knowledge of proteins with long-range dependencies by attention-enhanced GRNN to more accurately represent the complex interactions within protein complexes. The results indicate that the proposed approach outperforms existing state-of-the-art techniques. The approach represents a significant advancement in the computational cancer prediction of PPIs, providing a reliable method for researchers to detect possible interaction sites and better understand protein functions. The importance of the research lies in its potential to expedite the identification of novel pharmacological targets and therapeutic approaches, ultimately leading to progress in precision medicine and personalised healthcare.</p>