title: Predicting drug-target interactions using probabilistic matrix factorization.
abstract: Quantitative analysis of known drug-target interactions emerged in recent years as a useful approach for drug repurposing and assessing side effects. In the present study, we present a method that uses probabilistic matrix factorization (PMF) for this purpose, which is particularly useful for analyzing large interaction networks. DrugBank drugs clustered based on PMF latent variables show phenotypic similarity even in the absence of 3D shape similarity. Benchmarking computations show that the method outperforms those recently introduced provided that the input data set of known interactions is sufficiently large--which is the case for enzymes and ion channels, but not for G-protein coupled receptors (GPCRs) and nuclear receptors. Runs performed on DrugBank after hiding 70% of known interactions show that, on average, 88 of the top 100 predictions hit the hidden interactions. De novo predictions permit us to identify new potential interactions. Drug-target pairs implicated in neurobiological disorders are overrepresented among de novo predictions.
authors.affiliation: Department of Computational & Systems Biology and ‡Department of Pathology, School of Medicine, University of Pittsburgh , Pennsylvania 15213, United States.
authors.forename: Murat Can
authors.initials: MC
authors.lastname: Cobanoglu
authors.forename: Chang
authors.initials: C
authors.lastname: Liu
authors.forename: Feizhuo
authors.initials: F
authors.lastname: Hu
authors.forename: Zoltán N
authors.initials: ZN
authors.lastname: Oltvai
authors.forename: Ivet
authors.initials: I
authors.lastname: Bahar
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001665:Binding Sites; D016000:Cluster Analysis; D062313:Databases, Pharmaceutical; D030562:Databases, Protein; D055808:Drug Discovery; D058492:Drug Repositioning; D006801:Humans; D007473:Ion Channels / Q000819:agonists / Q000037:antagonists & inhibitors / Q000737:chemistry*; D055553:Prescription Drugs / Q000737:chemistry*; D011336:Probability; D018160:Receptors, Cytoplasmic and Nuclear / Q000819:agonists / Q000037:antagonists & inhibitors / Q000737:chemistry*; D043562:Receptors, G-Protein-Coupled / Q000819:agonists / Q000037:antagonists & inhibitors / Q000737:chemistry*
chemical_list: D007473:Ion Channels; D055553:Prescription Drugs; D018160:Receptors, Cytoplasmic and Nuclear; D043562:Receptors, G-Protein-Coupled