A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information

Abstract

The emergence of large-scale genomic, chemical and pharmacological data provides new opportunities for drug discovery and repositioning. In this work, we develop a computational pipeline, called DTINet, to predict novel drug–target interactions from a constructed heterogeneous network, which integrates diverse drug-related information. DTINet focuses on learning a low-dimensional vector representation of features, which accurately explains the topological properties of individual nodes in the heterogeneous network, and then makes prediction based on these representations via a vector space projection scheme. DTINet achieves substantial performance improvement over other state-of-the-art methods for drug–target interaction prediction. Moreover, we experimentally validate the novel interactions between three drugs and the cyclooxygenase proteins predicted by DTINet, and demonstrate the new potential applications of these identified cyclooxygenase inhibitors in preventing inflammatory diseases. These results indicate that DTINet can provide a practically useful tool for integrating heterogeneous information to predict new drug–target interactions and repurpose existing drugs.

Abstract

Network-based data integration for drug–target prediction is a promising avenue for drug repositioning, but performance is wanting. Here, the authors introduce DTINet, whose performance is enhanced in the face of noisy, incomplete and high-dimensional biological data by learning low-dimensional vector representations.

Contributors

Jian Peng: [e-mail]

Ligong Chen: [e-mail]

Jianyang Zeng: [e-mail]

Journal

Journal ID (nlm-ta): Nat Commun

Journal ID (iso-abbrev): Nat Commun

Title: Nature Communications

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2041-1723

Publication date (Electronic): 18 September 2017

Publication date PMC-release: 18 September 2017

Publication date Collection: 2017

Volume: 8

Affiliations

[1 ]ISNI 0000 0001 0662 3178, GRID grid.12527.33, Institute for Interdisciplinary Information Sciences, , Tsinghua University, ; Beijing, 100084 China

[2 ]ISNI 0000 0004 1936 9991, GRID grid.35403.31, Department of Computer Science, , University of Illinois at Urbana-Champaign, ; Urbana, IL 61801 USA

[3 ]ISNI 0000 0001 0662 3178, GRID grid.12527.33, School of Pharmaceutical Sciences, , Tsinghua University, ; Beijing, 100084 China

[4 ]ISNI 0000 0001 0807 1581, GRID grid.13291.38, Collaborative Innovation Center for Biotherapy, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, , Sichuan University, ; Chengdu, 610041 China

Article

Publisher ID: 680

DOI: 10.1038/s41467-017-00680-8

PMC ID: 5603535

PubMed ID: 28924171

Copyright statement: © The Author(s) 2017

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

Categories

Subject: Article

Custom metadata

issue-copyright-statement © The Author(s) 2017