Prediction of Drug Synergy and Antagonism Based on Drug-Drug Interaction Network

Wenbin LIU; Jie CHEN; Gang FANG; Xiaolong SHI; Peng XU

doi:10.11999/JEIT190867

Volume 42 Issue 6

Jun. 2020

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Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(6): 1420-1427

Wenbin LIU, Jie CHEN, Gang FANG, Xiaolong SHI, Peng XU. Prediction of Drug Synergy and Antagonism Based on Drug-Drug Interaction Network[J]. Journal of Electronics & Information Technology, 2020, 42(6): 1420-1427. doi: 10.11999/JEIT190867

Citation:

Wenbin LIU, Jie CHEN, Gang FANG, Xiaolong SHI, Peng XU. Prediction of Drug Synergy and Antagonism Based on Drug-Drug Interaction Network[J]. Journal of Electronics & Information Technology, 2020, 42(6): 1420-1427. doi: 10.11999/JEIT190867

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Prediction of Drug Synergy and Antagonism Based on Drug-Drug Interaction Network

doi: 10.11999/JEIT190867

Wenbin LIU^{1, 2
,
,},
Jie CHEN¹,
Gang FANG²,
Xiaolong SHI²,
Peng XU^{2, 3}

1.
College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China
2.
Institute of Computing Science and Technology, Guangzhou University, Guangzhou 510006, China
3.
School of Computer Science and Information Technology, Qiannan Normal University for Nationalities, Duyun 558000, China

Funds: The National Key R&D Program of China (2019YFA0706402), The National Natural Science Foundation of China (61572367, 61573017, 61972107, 61972109)

Received Date: 2019-11-01
Rev Recd Date: 2020-01-15

Available Online: 2020-02-18

Publish Date: 2020-06-22

Abstract

Abstract

Accurately predicting the synergistic and antagonistic relationship of drugs is helpful to the safety of drug use and the development of drug combination. A method for predicting drug synergy and antagonistic is proposed, which based on the Drug-Drug Interaction Network (DDINet) and its topological structure. From the result of feature selection, it can be seen that the feature constructed based on the interaction between the drug and its common neighbor node shows an obvious difference in the distribution of positive and negative samples, which can effectively reflect the drug synergy or antagonism. In the classification results using different feature classifiers, the optimal Area Under the Curve (AUC) and classification accuracy value reache 0.9687 and 0.9187 respectively. In the prediction results of synergy and antagonism, the prediction accuracy also reache above 0.45 and 0.75. This shows that the method based on network topology can effectively classify and predict the synergistic and antagonistic effects of drugs. Compared with the traditional methods based on similarity features of drug function, structure, target gene, etc, this method is simple and efficient to calculate, and can effectively promote the development of combination drugs.
- Prediction Drug-Drug Interactions(DDIs),
- Network topology,
- Synergy,
- Antagonism

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References(27)

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