Exploiting Heterogeneous Graphs with GCN for Drug Repositioning

In the realm of health concerns, drug development remains a formidable challenge due to its historical intricacy and costs. Addressing incurable diseases necessitates effective drugs, making the search for new therapeutic uses of existing drugs, known as drug repositioning, an invaluable strategy. This approach streamlines research and development, reducing expenses, as the safety of these drugs has been previously validated in trials. Over the last decade, the medical data landscape has expanded exponentially, providing researchers with an abundance of open-source data. Over time, continuously open-sourced data has shaped a heterogeneous network based on disease information. Deep learning's strong data mining capabilities in graphs have been extensively tested, and it demonstrates reliable extraction abilities, particularly when dealing with sparse data in heterogeneous graphs. The structural complexities of heterogeneous graphs call for adaptive methods. Among these, the Graph Convolutional Neural Network (GCN) stands out as a stable and widely applied deep learning technique in various domains. GCN efficiently extracts node features from graphs, thereby furnishing robust data support, either directly or indirectly, for drug repositioning endeavors. Enhanced GCN algorithms have the potential to significantly improve the extraction of hidden insights pertaining to diseases and drugs within heterogeneous graphs. This advancement promises to further accelerate progress in the field of drug repositioning while leveraging the expanding repository of medical data.