Prediction of Drug Synergy and Antagonism Based on Drug-Drug Interaction Network
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摘要: 药物的协同与拮抗关系预测，有助于药物的使用安全及组合用药的发展。该文从药物互作网络(DDINet)出发，基于网络拓扑结构构造分类特征，提出一种预测药物协同和拮抗关系的方法。从特征选择结果可知，根据药物与其公共邻居节点关系构造的特征表现出了明显的正负样本分布差距，能有效地反映出药物的协同或拮抗关系。在使用不同特征分类器的分类结果中，最优AUC和分类精度值分别达到了0.9687和0.9187。而在协同与拮抗关系预测结果中，其预测精度值达到了0.45和0.75以上。这说明基于网络拓扑结构的方法能有效对药物协同和拮抗关系进行分类和预测。与传统基于药物功能、结构、靶基因等相似性特征的方法相比，该方法计算简单高效，将会有效促进组合用药的发展。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.
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Key words:
- Prediction Drug-Drug Interactions(DDIs) /
- Network topology /
- Synergy /
- Antagonism
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