Graph-augmented Convolutional Networks on Drug-Drug Interactions Prediction
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2019
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| _version_ | 1866913917703290880 |
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| author | Zhong, Yi Chen, Xueyu Zhao, Yu Chen, Xiaoming Gao, Tingfang Weng, Zuquan |
| author_facet | Zhong, Yi Chen, Xueyu Zhao, Yu Chen, Xiaoming Gao, Tingfang Weng, Zuquan |
| contents | We propose an end-to-end model to predict drug-drug interactions (DDIs) by employing graph-augmented convolutional networks. And this is implemented by combining graph CNN with an attentive pooling network to extract structural relations between drug pairs and make DDI predictions. The experiment results suggest a desirable performance achieving ROC at 0.988, F1-score at 0.956, and AUPR at 0.986. Besides, the model can tell how the two DDI drugs interact structurally by varying colored atoms. And this may be helpful for drug design during drug discovery. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1912_03702 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Graph-augmented Convolutional Networks on Drug-Drug Interactions Prediction Zhong, Yi Chen, Xueyu Zhao, Yu Chen, Xiaoming Gao, Tingfang Weng, Zuquan Machine Learning We propose an end-to-end model to predict drug-drug interactions (DDIs) by employing graph-augmented convolutional networks. And this is implemented by combining graph CNN with an attentive pooling network to extract structural relations between drug pairs and make DDI predictions. The experiment results suggest a desirable performance achieving ROC at 0.988, F1-score at 0.956, and AUPR at 0.986. Besides, the model can tell how the two DDI drugs interact structurally by varying colored atoms. And this may be helpful for drug design during drug discovery. |
| title | Graph-augmented Convolutional Networks on Drug-Drug Interactions Prediction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/1912.03702 |