Prediction of drug-target interaction networks from the integration of chemical and genomic spaces

Yoshihiro Yamanishi, Michihiro Araki, Alex Gutteridge, Wataru Honda, Minoru Kanehisa

    Research output: Contribution to journalArticle

    448 Citations (Scopus)

    Abstract

    Motivation: The identification of interactions between drugs and target proteins is a key area in genomic drug discovery. Therefore, there is a strong incentive to develop new methods capable of detecting these potential drug-target interactions efficiently. Results: In this article, we characterize four classes of drug-target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, and reveal significant correlations between drug structure similarity, target sequence similarity and the drug-target interaction network topology. We then develop new statistical methods to predict unknown drug-target interaction networks from chemical structure and genomic sequence information simultaneously on a large scale. The originality of the proposed method lies in the formalization of the drug-target interaction inference as a supervised learning problem for a bipartite graph, the lack of need for 3D structure information of the target proteins, and in the integration of chemical and genomic spaces into a unified space that we call 'pharmacological space'. In the results, we demonstrate the usefulness of our proposed method for the prediction of the four classes of drug-target interaction networks. Our comprehensively predicted drug-target interaction networks enable us to suggest many potential drug-target interactions and to increase research productivity toward genomic drug discovery.

    Original languageEnglish
    Pages (from-to)i232-i240
    JournalBioinformatics
    Volume24
    Issue number13
    DOIs
    Publication statusPublished - Jul 1 2008

    Fingerprint

    Drug Interactions
    Genomics
    Drugs
    Proteins
    Target
    Prediction
    Interaction
    Drug interactions
    Pharmaceutical Preparations
    Supervised learning
    Statistical methods
    Enzymes
    Productivity
    Topology
    Drug Discovery
    Ions
    Receptor
    Protein
    G Protein
    Cytoplasmic and Nuclear Receptors

    All Science Journal Classification (ASJC) codes

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

    Cite this

    Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. / Yamanishi, Yoshihiro; Araki, Michihiro; Gutteridge, Alex; Honda, Wataru; Kanehisa, Minoru.

    In: Bioinformatics, Vol. 24, No. 13, 01.07.2008, p. i232-i240.

    Research output: Contribution to journalArticle

    Yamanishi, Y, Araki, M, Gutteridge, A, Honda, W & Kanehisa, M 2008, 'Prediction of drug-target interaction networks from the integration of chemical and genomic spaces', Bioinformatics, vol. 24, no. 13, pp. i232-i240. https://doi.org/10.1093/bioinformatics/btn162
    Yamanishi, Yoshihiro ; Araki, Michihiro ; Gutteridge, Alex ; Honda, Wataru ; Kanehisa, Minoru. / Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. In: Bioinformatics. 2008 ; Vol. 24, No. 13. pp. i232-i240.
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