title: Supervised prediction of drug-target interactions using bipartite local models.
abstract: BACKGROUND
  In silico prediction of drug-target interactions from heterogeneous biological data is critical in the search for drugs for known diseases. This problem is currently being attacked from many different points of view, a strong indication of its current importance. Precisely, being able to predict new drug-target interactions with both high precision and accuracy is the holy grail, a fundamental requirement for in silico methods to be useful in a biological setting. This, however, remains extremely challenging due to, amongst other things, the rarity of known drug-target interactions.
  
  
  RESULTS
  We propose a novel supervised inference method to predict unknown drug-target interactions, represented as a bipartite graph. We use this method, known as bipartite local models to first predict target proteins of a given drug, then to predict drugs targeting a given protein. This gives two independent predictions for each putative drug-target interaction, which we show can be combined to give a definitive prediction for each interaction. We demonstrate the excellent performance of the proposed method in the prediction of four classes of drug-target interaction networks involving enzymes, ion channels, G protein-coupled receptors (GPCRs) and nuclear receptors in human. This enables us to suggest a number of new potential drug-target interactions.
  
  
  BACKGROUND
  An implementation of the proposed algorithm is available upon request from the authors. Datasets and all prediction results are available at http://cbio.ensmp.fr/~yyamanishi/bipartitelocal/.
authors: Mines ParisTech, Centre for Computational Biology, Fontainebleau, France. kevbleakley@gmail.com
authors: Kevin
authors: K
authors: Bleakley
authors: Yoshihiro
authors: Y
authors: Yamanishi
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D055808:Drug Discovery; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D043562:Receptors, G-Protein-Coupled / Q000378:metabolism
chemical_list: D004364:Pharmaceutical Preparations; D011506:Proteins; D043562:Receptors, G-Protein-Coupled