Large-scale Prediction of Drug-Protein Interactions Based on Network Information
Xinsheng Li, Daichuan Ma, Yan Dong Ren, Jiesi Luo, Yizhou Li · Current Computer - Aided Drug Design · 2021
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.