Predicting drug-target interactions using probabilistic matrix factorization

Murat Can Cobanoglu, Chang Liu, Feizhuo Hu, Zoltán N. Oltvai, Ivet Bahar

Research output: Contribution to journalArticlepeer-review

76 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)3399-3409
Number of pages11
JournalJournal of Chemical Information and Modeling
Issue number12
StatePublished - Dec 23 2013


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