Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference

Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In…

• NN
  National Natural Science Foundation of China

  Awards: 21072059, 11DZ2260600, 10ZZ41, WY1113007, 2012AA020308
• SM
  Shanghai Municipal Education Commission

  Awards: 21072059, 11DZ2260600, 10ZZ41, WY1113007
• HE
  Higher Education Discipline Innovation Project

  Awards: B07023, Grant B07023
• FR
  Fundamental Research Funds for the Central Universities

  Awards: 21072059, 10ZZ41, B07023, WY1113007, 11DZ2260600