RGDA-DDI: Residual graph attention network and dual-attention based framework for drug-drug interaction prediction
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929477597003776 |
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| author | Zhou, Changjian Zhang, Xin Li, Jiafeng Song, Jia Xiang, Wensheng |
| author_facet | Zhou, Changjian Zhang, Xin Li, Jiafeng Song, Jia Xiang, Wensheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_15310 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RGDA-DDI: Residual graph attention network and dual-attention based framework for drug-drug interaction prediction Zhou, Changjian Zhang, Xin Li, Jiafeng Song, Jia Xiang, Wensheng Molecular Networks Computational Engineering, Finance, and Science Machine Learning 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. |
| title | RGDA-DDI: Residual graph attention network and dual-attention based framework for drug-drug interaction prediction |
| topic | Molecular Networks Computational Engineering, Finance, and Science Machine Learning |
| url | https://arxiv.org/abs/2408.15310 |