BACKGROUND The prediction of drug-protein interaction (DPI) plays an important role in drug discovery and repositioning. Unfortunately, traditional experimental validation of DPIs is expensive and time-consuming. Therefore, it is necessary to develop in silico methods for the identification of potential DPIs. METHODS In this work, the identification of DPIs was performed by the generated recommendation of the unexplored interaction of the drug-protein bipartite graph. Three kinds of recommenders were proposed to predict the potential DPIs. RESULTS The simulation results showed that the proposed models obtained good performance in crossvalidation and independent test. CONCLUSION Our recommendation strategy based on collaborative filtering can effectively improve the DPI identification performance, especially for certain DPIs lacking chemical structure similarity or genomic sequence similarity.

中文翻译：

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基于网络信息的药物-蛋白质相互作用的大规模预测。

背景技术药物-蛋白质相互作用（DPI）的预测在药物发现和重新定位中起着重要作用。不幸的是，传统的 DPI 实验验证既昂贵又耗时。因此，有必要开发计算机方法来识别潜在的 DPI。方法 在这项工作中，DPI 的识别是通过生成的药物-蛋白质二分图的未探索相互作用的建议来进行的。提出了三种推荐器来预测潜在的 DPI。结果仿真结果表明，所提出的模型在交叉验证和独立测试中取得了良好的性能。结论 我们基于协同过滤的推荐策略可以有效提高 DPI 识别性能，