DDI-KGAT: A Graph Attention Network on Biomedical Knowledge Graph for the Prediction of Drug-Drug Interactions

Abstract

Effective drug combination prediction is crucial for the success of drug discovery, but it is a challenging task due to drug-drug interactions and potential adverse drug reactions. In this work, a novel technique to DDI prediction using knowledge graph-based approach called KGAT is proposed, which utilizes attention mechanisms with graph convolution layers to capture important features and correlations between drugs and other entities such as targets and genes. Our model employs attention mechanisms to prioritize significant interactions and aggregates information through sum, mean, and max operations to enhance prediction accuracy. This allows KGAT to effectively mine high-order structures and semantic relationships within the knowledge graph. We evaluate our model on the KEGG dataset and compare its performance with existing state-of-the-art methods. The results show that KGAT outperforms these methods. Additionally, our approach has several advantages, including simplicity, interpretability, and low-dimensional complexity, making it a promising tool for accelerating drug discovery and development. By identifying novel drug combinations with improved efficacy and safety profiles, our approach has the potential to improve patient outcomes and support safer drug development. Our study highlights the potential of attention mechanisms in knowledge graph-based drug combination prediction, and we believe that KGAT can serve as a valuable framework for future research in this field.