Supervised prediction of drug-target interactions using bipartite local models

Kevin Bleakley, Yoshihiro Yamanishi

    Research output: Contribution to journalArticle

    264 Citations (Scopus)

    Abstract

    Motivation: 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.

    Original languageEnglish
    Pages (from-to)2397-2403
    Number of pages7
    JournalBioinformatics
    Volume25
    Issue number18
    DOIs
    Publication statusPublished - Sep 1 2009

    Fingerprint

    Drug Interactions
    Drugs
    Target
    Prediction
    Proteins
    Interaction
    Pharmaceutical Preparations
    Computer Simulation
    Predict
    Model
    Enzymes
    Receptor
    Drug Delivery Systems
    Cytoplasmic and Nuclear Receptors
    G-Protein-Coupled Receptors
    Ion Channels
    Ions
    Protein
    G Protein
    Bipartite Graph

    All Science Journal Classification (ASJC) codes

    • Statistics and Probability
    • Biochemistry
    • Molecular Biology
    • Computer Science Applications
    • Computational Theory and Mathematics
    • Computational Mathematics

    Cite this

    Supervised prediction of drug-target interactions using bipartite local models. / Bleakley, Kevin; Yamanishi, Yoshihiro.

    In: Bioinformatics, Vol. 25, No. 18, 01.09.2009, p. 2397-2403.

    Research output: Contribution to journalArticle

    Bleakley, Kevin ; Yamanishi, Yoshihiro. / Supervised prediction of drug-target interactions using bipartite local models. In: Bioinformatics. 2009 ; Vol. 25, No. 18. pp. 2397-2403.
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