Jong-Hoon Park
Department of Computer Science
Yonsei University - Mirae Campus
Young-Rae Cho
Division of Software
Yonsei University - Mirae Campus
Drug repositioning, a strategy that repurposes already-approved drugs for novel therapeutic applications, provides a faster and more cost-effective alternative to traditional drug discovery.
Network-based models have been adopted by many computational methodologies, especially those that use graph neural networks to predict drug-disease associations.
However, these techniques frequently overlook the quality of the input network, which is a critical factor for achieving accurate predictions.
We present a novel network-based framework for drug repositioning, named DRAW+, which incorporates noise filtering and feature extraction using graph neural networks and attention mechanisms.
The proposed model first constructs a heterogeneous network that integrates the drugdisease association network with the similarity networks of drugs and diseases, which are upgraded through reduced-rank singular value decomposition.
Next, a subgraph surrounding the targeted drugdisease node pair is extracted, allowing the model to focus on local structures.
Graph neural networks are then applied to extract structural representation, followed by attention walking to capture key features of the subgraph.
Finally, a multi-layer perceptron classifies the subgraph as positive or negative, which indicates the presence of the link between the target node pair.
Experimental validation across three benchmark datasets showed that DRAW+ outperformed seven state-of-the-art methods, achieving the highest average AUROC and AUPRC, 0.963 and 0.564, respectively.
Moreover, DRAW+ demonstrated its robustness by achieving the best performance across two additional datasets, further confirming its generalizability and effectiveness in diverse settings.
The proposed network-based computational approach, DRAW+, demonstrates exceptional accuracy and robustness, confirming its effectiveness in drug repositioning tasks.