Faculty Publications
Document Type
Article
Journal/Book/Conference Title Title
PLoS ONE Computational Biology
Volume
12
Issue
10
Abstract
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.
Department
Department of Computer Science
Original Publication Date
10-2016
DOI of published version
10.1371/journal.pcbi.1005135
Repository
UNI ScholarWorks, University of Northern Iowa, Rod Library
Copyright
©2016 Hansim Lim, Alexsander Poleksic, Yuan Yao, Hanghang Tong, Di He, Luke Zhuang, Patrick Meng, and Lei Xie. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Date Digital
2016
Language
EN
File Format
application/pdf
Recommended Citation
Lim, Hansaim; Poleksic, Alexsandar; Yao, Yuan; Tong, Hanghang; He, Di; Zhuang, Luke; Meng, Patrick; and Xie, Lei, "Large-ScaleOff-Target IdentificationUsing Fast and Accurate Dual Regularized OneClass Collaborative Filtering and Its Application to Drug Repurposing" (2016). Faculty Publications. 4.
https://scholarworks.uni.edu/cmp_facpub/4
Comments
First published in PLoS ONE, v.12 n.10 (2017) e1005135, published by the Public Library of Science. DOI: https://doi.org/10.1371/journal.pcbi.1005135
Correction
3 Jan 2017: The PLOS Computational Biology Staff (2017) Correction: Large-Scale Off-Target Identification Using Fast and Accurate Dual Regularized One-Class Collaborative Filtering and Its Application to Drug Repurposing. PLOS Computational Biology 13(1): e1005312. https://doi.org/10.1371/journal.pcbi.1005312 View correction