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dc.contributor.authorThafar, Maha A.
dc.contributor.authorAlshahrani, Mona
dc.contributor.authorAlbaradei, Somayah
dc.contributor.authorGojobori, Takashi
dc.contributor.authorEssack, Magbubah
dc.contributor.authorGao, Xin
dc.date.accessioned2022-04-20T13:46:55Z
dc.date.available2022-04-20T13:46:55Z
dc.date.issued2022-03-19
dc.identifier.citationThafar, M. A., Alshahrani, M., Albaradei, S., Gojobori, T., Essack, M., & Gao, X. (2022). Affinity2Vec: drug-target binding affinity prediction through representation learning, graph mining, and machine learning. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-08787-9
dc.identifier.issn2045-2322
dc.identifier.pmid35306525
dc.identifier.doi10.1038/s41598-022-08787-9
dc.identifier.urihttp://hdl.handle.net/10754/676354
dc.description.abstractDrug-target interaction (DTI) prediction plays a crucial role in drug repositioning and virtual drug screening. Most DTI prediction methods cast the problem as a binary classification task to predict if interactions exist or as a regression task to predict continuous values that indicate a drug's ability to bind to a specific target. The regression-based methods provide insight beyond the binary relationship. However, most of these methods require the three-dimensional (3D) structural information of targets which are still not generally available to the targets. Despite this bottleneck, only a few methods address the drug-target binding affinity (DTBA) problem from a non-structure-based approach to avoid the 3D structure limitations. Here we propose Affinity2Vec, as a novel regression-based method that formulates the entire task as a graph-based problem. To develop this method, we constructed a weighted heterogeneous graph that integrates data from several sources, including drug-drug similarity, target-target similarity, and drug-target binding affinities. Affinity2Vec further combines several computational techniques from feature representation learning, graph mining, and machine learning to generate or extract features, build the model, and predict the binding affinity between the drug and the target with no 3D structural data. We conducted extensive experiments to evaluate and demonstrate the robustness and efficiency of the proposed method on benchmark datasets used in state-of-the-art non-structured-based drug-target binding affinity studies. Affinity2Vec showed superior and competitive results compared to the state-of-the-art methods based on several evaluation metrics, including mean squared error, rm2, concordance index, and area under the precision-recall curve.
dc.description.sponsorshipSupported by King Abdullah University of Science and Technology (KAUST) through grant awards Nos. BAS/1/1059-01-01, BAS/1/1624-01-01, FCC/1/1976-20-01, FCC/1/1976-26-01, URF/1/3450-01-01, REI/1/4216-01-01, REI/1/4437-01-01, REI/1/4473-01-01, and URF/1/4098-01-01
dc.publisherSpringer Science and Business Media LLC
dc.relation.urlhttps://www.nature.com/articles/s41598-022-08787-9
dc.rightsArchived with thanks to Scientific Reports under a Creative Commons license, details at: https://creativecommons.org/licenses/by/4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleAffinity2Vec: drug-target binding affinity prediction through representation learning, graph mining, and machine learning
dc.typeArticle
dc.contributor.departmentStructural and Functional Bioinformatics Group
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.contributor.departmentBioscience Program
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.identifier.journalScientific Reports
dc.identifier.pmcidPMC8934358
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionCollege of Computers and Information Technology, Taif University, Taif, Saudi Arabia
dc.contributor.institutionNational Center for Artificial Intelligence (NCAI), Saudi Data and Artificial Intelligence Authority (SDAIA), Riyadh, Saudi Arabia
dc.contributor.institutionFaculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
dc.identifier.volume12
dc.identifier.issue1
kaust.personThafar, Maha A.
kaust.personAlbaradei, Somayah
kaust.personGojobori, Takashi
kaust.personEssack, Magbubah
kaust.personGao, Xin
kaust.grant.numberBAS/1/1059-01-01
kaust.grant.numberBAS/1/1624-01-01
kaust.grant.numberFCC/1/1976-20-01
kaust.grant.numberFCC/1/1976-26-01
kaust.grant.numberREI/1/4216-01-01
kaust.grant.numberREI/1/4437-01-01
kaust.grant.numberREI/1/4473-01-01
kaust.grant.numberURF/1/3450-01-01
kaust.grant.numberURF/1/4098-01-01
dc.relation.issupplementedbygithub:MahaThafar/Affinity2Vec
dc.identifier.eid2-s2.0-85126778930
refterms.dateFOA2022-04-20T13:48:24Z
display.relations<b>Is Supplemented By:</b><br/> <ul><li><i>[Software]</i> <br/> Title: MahaThafar/Affinity2Vec: Drug-target binding affinity prediction using representation learning, graph mining, and machine learning. Publication Date: 2021-04-05. github: <a href="https://github.com/MahaThafar/Affinity2Vec" >MahaThafar/Affinity2Vec</a> Handle: <a href="http://hdl.handle.net/10754/676685" >10754/676685</a></a></li></ul>


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Archived with thanks to Scientific Reports under a Creative Commons license, details at: https://creativecommons.org/licenses/by/4.0
Except where otherwise noted, this item's license is described as Archived with thanks to Scientific Reports under a Creative Commons license, details at: https://creativecommons.org/licenses/by/4.0