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Design of a tripartite network for the prediction of drug targets

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Abstract

Drug-target networks have aided in many target prediction studies aiming at drug repurposing or the analysis of side effects. Conventional drug-target networks are bipartite. They contain two different types of nodes representing drugs and targets, respectively, and edges indicating pairwise drug-target interactions. In this work, we introduce a tripartite network consisting of drugs, other bioactive compounds, and targets from different sources. On the basis of analog relationships captured in the network and so-called neighbor targets of drugs, new drug targets can be inferred. The tripartite network was found to have a stable structure and simulated network growth was accompanied by a steady increase in assortativity, reflecting increasing correlation between degrees of connected nodes leading to even network connectivity. Local drug environments in the tripartite network typically contained neighbor targets and revealed interesting drug-compound-target relationships for further analysis. Candidate targets were prioritized. The tripartite network design extends standard drug-target networks and provides additional opportunities for drug target prediction.

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Acknowledgements

We thank the OpenEye Scientific Software, Inc., for providing a free academic license of the OpenEye chemistry toolkit.

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Authors and Affiliations

  1. Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2, 53113, Bonn, Germany

    Ryo Kunimoto & Jürgen Bajorath

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  1. Ryo Kunimoto
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  2. Jürgen Bajorath
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Correspondence to Jürgen Bajorath.

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Kunimoto, R., Bajorath, J. Design of a tripartite network for the prediction of drug targets. J Comput Aided Mol Des 32, 321–330 (2018). https://doi.org/10.1007/s10822-018-0098-x

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  • DOI: https://doi.org/10.1007/s10822-018-0098-x

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