MTP-DDA: Enhanced Drug-Disease Associations Prediction via Multi-task Learning with Multi-view Graph Convolutional Networks and Contrastive Learning

Abstract

Predicting drug-disease associations (DDAs) plays a crucial role in drug development and disease treatment. However, existing researches predominantly focus on single DDAs prediction task, often overlooking the intricate relationships among different tasks, which can further improve the performance of methods for DDAs prediction. To address this limitation, a multi-task prediction framework, capable of simultaneously predicting drug-disease, drug-protein, and disease-protein associations, is proposed, named MTP-DDA. The framework constructs three distinct graphs to reflect different relationships between biological entities. Then, based on these graphs, two sub-views and one main-view are constructed. For sub-view, corruption strategy is adopted to generate corrupted view, and Graph Convolutional Network (GCN) is employed to extract features from both the original view and its corrupted version, with contrastive learning applied to enhance feature representations. For main-view, GCN and Node2Vec are utilized to extract low-order and high-order node features respectively, and an attention mechanism is utilized for feature fusion. Finally, the node features from three views above are integrated, and the dot product operation is applied to the node features of association pairs to derive association scores, thereby enabling multi-task association prediction. Under 10-fold cross-validation, the proposed framework outperforms current methods on public datasets, demonstrating its effectiveness and robustness.