Large-scale Prediction of Drug-Protein Interactions Based on Network Information

Abstract

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.

Keywords: Drug discovery; Jaccard index.; bipartite graph; collaborative filtering; drug-protein interaction; recommender system.