RGDA-DDI: Residual graph attention network and dual-attention based framework for drug-drug interaction prediction
This work addresses drug development challenges by enhancing computational prediction of drug-drug interactions, though it appears incremental as it builds on existing graph-based methods.
The paper tackled drug-drug interaction prediction by proposing RGDA-DDI, a framework using residual graph attention networks and dual-attention, which significantly improved performance on two public benchmark datasets.
Recent studies suggest that drug-drug interaction (DDI) prediction via computational approaches has significant importance for understanding the functions and co-prescriptions of multiple drugs. However, the existing silico DDI prediction methods either ignore the potential interactions among drug-drug pairs (DDPs), or fail to explicitly model and fuse the multi-scale drug feature representations for better prediction. In this study, we propose RGDA-DDI, a residual graph attention network (residual-GAT) and dual-attention based framework for drug-drug interaction prediction. A residual-GAT module is introduced to simultaneously learn multi-scale feature representations from drugs and DDPs. In addition, a dual-attention based feature fusion block is constructed to learn local joint interaction representations. A series of evaluation metrics demonstrate that the RGDA-DDI significantly improved DDI prediction performance on two public benchmark datasets, which provides a new insight into drug development.