DeepTransformer: Node Classification Research of a Deep Graph Network on an Osteoporosis Graph based on GraphTransformer

Background: Osteoporosis (OP) is one of the most common diseases in the elderly population. It is mostly treated with medication, but drug research and development have the disadvantage of taking a long time and having a high cost. Objective: Therefore, we developed a graph neural network with the help of artificial intelligence to provide new ideas for drug research and development for OP. Methods: In this study, we built a new osteoporosis graph (called OPGraph) and proposed a deep graph neural network (called DeepTransformer) to predict new drugs for OP. OPGraph is a graph data model established by gathering features and their interrelationships from a vast amount of OP data. DeepTransformer uses GraphTransformer as its foundational network and applies residual connections for deep layering. Results: The analysis and results showed that DeepTransformer outperformed numerous models on OPGraph, with area under the curve (AUC) and area under the precision-recall curve (AUPR) reaching 0.9916 and 0.9911, respectively. In addition, we conducted an in vitro validation experiment on two of the seven predicted compounds (Puerarin and Aucubin), and the results corroborated the predictions of our model. Conclusion: The model we developed with the help of artificial intelligence can effectively reduce the time and cost of OP drug development and reduce the heavy economic burden brought to patient's family by complications caused by osteoporosis.