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Linear and Kernel Model Construction Methods for Predicting Drug–Target Interactions in a Chemogenomic Framework

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Computational Chemogenomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1825))

Abstract

Identification of drug–target interactions is a crucial process in drug discovery. In this chapter, we present protocols for recent advancements in machine learning methods for predicting drug–target interactions from heterogeneous biological data in a chemogenomic framework, in which prediction is based on the chemical structure data of drug candidate compounds and translated genomic sequence data of target candidate proteins. Most existing methods are based on either linear modeling or kernel modeling. To illustrate linear modeling, we introduce sparsity-induced binary classifiers and sparse canonical correlation analysis. To illustrate kernel modeling, we introduce pairwise kernel-based support vector machines and kernel-based distance learning. Workflows for using these techniques are presented. We also discuss the characteristics of each method and suggest some directions for future research.

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Acknowledgments

This work is supported by JST PRESTO Grant Number JPMJPR15D8.

Author information

Authors and Affiliations

  1. Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan

    Yoshihiro Yamanishi

  2. PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama, Japan

    Yoshihiro Yamanishi

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  1. Yoshihiro Yamanishi
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Corresponding author

Correspondence to Yoshihiro Yamanishi .

Editor information

Editors and Affiliations

  1. Life Science Informatics Research Unit, Laboratory of Molecular Biosciences, Kyoto University Graduate School of Medicine, Kyoto, Japan

    J.B. Brown

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Yamanishi, Y. (2018). Linear and Kernel Model Construction Methods for Predicting Drug–Target Interactions in a Chemogenomic Framework. In: Brown, J. (eds) Computational Chemogenomics. Methods in Molecular Biology, vol 1825. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8639-2_12

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  • DOI: https://doi.org/10.1007/978-1-4939-8639-2_12

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8638-5

  • Online ISBN: 978-1-4939-8639-2

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