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  4. Large-Scale Off-Target Identification Using Fast and Accurate Dual Regularized One-Class Collaborative Filtering and Its Application to Drug Repurposing
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Large-Scale Off-Target Identification Using Fast and Accurate Dual Regularized One-Class Collaborative Filtering and Its Application to Drug Repurposing

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Description

Target-based screening is one of the major approaches in drug discovery. Besides the intended target, unexpected drug off-target interactions often occur, and many of them have not been recognized and characterized. The off-target interactions can be responsible for either therapeutic or side effects. Thus, identifying the genome-wide off-targets of lead compounds or existing drugs will be critical for designing effective and safe drugs, and providing new opportunities for drug repurposing. Although many computational methods have been developed to predict drug-target interactions, they are either less accurate than the one that we are proposing here or computationally too intensive, thereby limiting their capability for large-scale off-target identification. In addition, the performances of most machine learning based algorithms have been mainly evaluated to predict off-target interactions in the same gene family for hundreds of chemicals. It is not clear how these algorithms perform in terms of detecting off-targets across gene families on a proteome scale.

Here, we are presenting a fast and accurate off-target prediction method, REMAP, which is based on a dual regularized one-class collaborative filtering algorithm, to explore continuous chemical space, protein space, and their interactome on a large scale. When tested in a reliable, extensive, and cross-gene family benchmark, REMAP outperforms the state-of-the-art methods. Furthermore, REMAP is highly scalable. It can screen a dataset of 200 thousands chemicals against 20 thousands proteins within 2 hours. Using the reconstructed genome-wide target profile as the fingerprint of a chemical compound, we predicted that seven FDA-approved drugs can be repurposed as novel anti-cancer therapies. The anti-cancer activity of six of them is supported by experimental evidences. Thus, REMAP is a valuable addition to the existing in silico toolbox for drug target identification, drug repurposing, phenotypic screening, and side effect prediction. The software and benchmark are available at https://github.com/hansaimlim/REMAP.

Date Created
2016-10-07
Contributors
  • Lim, Hansaim (Author)
  • Poleksic, Aleksandar (Author)
  • Yao, Yuan (Author)
  • Tong, Hanghang (Author)
  • He, Di (Author)
  • Zhuang, Luke (Author)
  • Meng, Patrick (Author)
  • Xie, Lei (Author)
  • Ira A. Fulton Schools of Engineering (Contributor)
Extent
26 pages
Language
eng
Copyright Statement
In Copyright
Reuse Permissions
Attribution
Primary Member of
ASU Scholarship Showcase
Identifier
Digital object identifier: 10.1371/journal.pcbi.1005135
Identifier Type
International standard serial number
Identifier Value
1553-734X
Identifier Type
International standard serial number
Identifier Value
1553-7358
Peer-reviewed
No
Open Access
No
Series
PLOS COMPUTATIONAL BIOLOGY
Handle
https://hdl.handle.net/2286/R.I.43700
Preferred Citation

Lim, H., Poleksic, A., Yao, Y., Tong, H., He, D., Zhuang, L., . . . Xie, L. (2016). Large-Scale Off-Target Identification Using Fast and Accurate Dual Regularized One-Class Collaborative Filtering and Its Application to Drug Repurposing. PLOS Computational Biology, 12(10). doi:10.1371/journal.pcbi.1005135

Level of coding
minimal
Cataloging Standards
asu1
Note
The article is published at http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005135, opens in a new window
System Created
  • 2017-05-19 03:00:54
System Modified
  • 2021-12-03 04:23:23
  •     
  • 1 year 3 months ago
Additional Formats
  • OAI Dublin Core
  • MODS XML

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