RGDA-DDI: Residual graph attention network and dual-attention based framework for drug-drug interaction prediction
<|reference_start|>RGDA-DDI: Residual graph attention network and dual-attention based framework for drug-drug interaction prediction: 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.<|reference_end|>
@article{zhou2024rgda-ddi:,
    title={RGDA-DDI: Residual graph attention network and dual-attention based
  framework for drug-drug interaction prediction},
    author={Changjian Zhou, Xin Zhang, Jiafeng Li, Jia Song, and Wensheng Xiang},
    journal={arXiv preprint arXiv:2408.15310},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.15310},
    primaryClass={q-bio.MN cs.CE cs.LG}
}