Title:Are Topological Properties of Drug Targets Based on Protein-Protein Interaction Network Ready to Predict Potential Drug Targets?
VOLUME: 19 ISSUE: 2
Author(s):Shiliang Li, Xiaojuan Yu, Chuanxin Zou, Jiayu Gong and Xiaofeng Liu
Affiliation:School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
Keywords:Drug targets, protein-protein interactions network, support vector machine, network topological properties.
Abstract:Identification of potential druggable targets utilizing protein-protein interactions network
(PPIN) has been emerging as a hotspot in drug discovery and development research. However, it
remains unclear whether the currently used PPIN topological properties are enough to discriminate the
drug targets from non-drug targets. In this study, three-step classification models using different
network topological properties were designed and implemented using support vector machine (SVM)
to compare the enrichment of known drug targets from non-targets. Surprisingly, none of the models
was able to identify more than 75% of the true targets in the test set. It appears that the currently used
simple PPIN topological properties are not likely robust enough for prediction of potential drug targets with high
confidence, which also echoes similarly unsatisfying prediction data reported previously. However, we proposed that
quality and quantity improvement of the protein-protein interactions (PPI) data for model training will help increasing the
prediction accuracy.
