title: Large-Scale Off-Target Identification Using Fast and Accurate Dual Regularized One-Class Collaborative Filtering and Its Application to Drug Repurposing.
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.
authors.affiliation: The Graduate Center, The City University of New York, New York, New York, United States.
authors.forename: Hansaim
authors.identifier: http://orcid.org/0000-0002-8420-4750
authors.initials: H
authors.lastname: Lim
authors.affiliation: Department of Computer Science, University of Northern Iowa, Cedar Falls, Iowa, United States.
authors.forename: Aleksandar
authors.initials: A
authors.lastname: Poleksic
authors.affiliation: Department of Computer Science and Technology, Nanjing University, Nanjing, Jiangsu, China.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Yao
authors.affiliation: School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona, United States.
authors.forename: Hanghang
authors.initials: H
authors.lastname: Tong
authors.affiliation: The Graduate Center, The City University of New York, New York, New York, United States.
authors.forename: Di
authors.identifier: http://orcid.org/0000-0002-0719-8289
authors.initials: D
authors.lastname: He
authors.affiliation: Academy for Information Technology, Union County Vocational-Technical Schools, Scotch Plains, New Jersey, United States.
authors.forename: Luke
authors.identifier: http://orcid.org/0000-0003-3892-384X
authors.initials: L
authors.lastname: Zhuang
authors.affiliation: High Technology High School, Lincroft, New Jersey, United States.
authors.forename: Patrick
authors.initials: P
authors.lastname: Meng
authors.affiliation: The Graduate Center, The City University of New York, New York, New York, United States.|Department of Computer Science, Hunter College, The City University of New York, New York, New York, United States.
authors.forename: Lei
authors.initials: L
authors.lastname: Xie
mesh_terms: D000970:Antineoplastic Agents / Q000737:chemistry*; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D058492:Drug Repositioning / Q000379:methods*; D057166:High-Throughput Screening Assays / Q000379:methods*; D058990:Molecular Targeted Therapy / Q000379:methods; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry*
chemical_list: D000970:Antineoplastic Agents; D011506:Proteins