College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, Wuhou District, Chengdu, Sichuan 610065,China.
Analytical & Testing Center, Sichuan University, No.29 Wangjiang Road, Wuhou District, Chengdu, Sichuan 610064,China.
Curr Comput Aided Drug Des. 2022;18(1):64-72. doi: 10.2174/1573409917666210315094213.
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.
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.
The simulation results showed that the proposed models obtained good performance in crossvalidation and independent test.
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.
药物-蛋白相互作用（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，有必要开发用于识别潜在 DPI 的计算方法。
在这项工作中，通过生成药物-蛋白二部图未探索相互作用的推荐来进行 DPI 的识别。提出了三种推荐器来预测潜在的 DPI。
模拟结果表明，所提出的模型在交叉验证和独立测试中均取得了良好的性能。
我们基于协同过滤的推荐策略可以有效地提高 DPI 识别性能，特别是对于某些缺乏化学结构相似性或基因组序列相似性的 DPI。
