Mining Chemical Activity Status from High-Throughput Screening Assays

Handle URI:
http://hdl.handle.net/10754/596363
Title:
Mining Chemical Activity Status from High-Throughput Screening Assays
Authors:
Soufan, Othman ( 0000-0002-4410-1853 ) ; Ba Alawi, Wail ( 0000-0002-2747-4703 ) ; Afeef, Moataz A.; Essack, Magbubah ( 0000-0003-2709-5356 ) ; Rodionov, Valentin; Kalnis, Panos ( 0000-0002-5060-1360 ) ; Bajic, Vladimir B. ( 0000-0001-5435-4750 )
Abstract:
High-throughput screening (HTS) experiments provide a valuable resource that reports biological activity of numerous chemical compounds relative to their molecular targets. Building computational models that accurately predict such activity status (active vs. inactive) in specific assays is a challenging task given the large volume of data and frequently small proportion of active compounds relative to the inactive ones. We developed a method, DRAMOTE, to predict activity status of chemical compounds in HTP activity assays. For a class of HTP assays, our method achieves considerably better results than the current state-of-the-art-solutions. We achieved this by modification of a minority oversampling technique. To demonstrate that DRAMOTE is performing better than the other methods, we performed a comprehensive comparison analysis with several other methods and evaluated them on data from 11 PubChem assays through 1,350 experiments that involved approximately 500,000 interactions between chemicals and their target proteins. As an example of potential use, we applied DRAMOTE to develop robust models for predicting FDA approved drugs that have high probability to interact with the thyroid stimulating hormone receptor (TSHR) in humans. Our findings are further partially and indirectly supported by 3D docking results and literature information. The results based on approximately 500,000 interactions suggest that DRAMOTE has performed the best and that it can be used for developing robust virtual screening models. The datasets and implementation of all solutions are available as a MATLAB toolbox online at www.cbrc.kaust.edu.sa/dramote and can be found on Figshare.
KAUST Department:
Computational Bioscience Research Center (CBRC); KAUST Catalysis Center (KCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Mining Chemical Activity Status from High-Throughput Screening Assays 2015, 10 (12):e0144426 PLOS ONE
Publisher:
Public Library of Science (PLoS)
Journal:
PLoS ONE
Issue Date:
14-Dec-2015
DOI:
10.1371/journal.pone.0144426
Type:
Article
ISSN:
1932-6203
Additional Links:
http://dx.plos.org/10.1371/journal.pone.0144426
Appears in Collections:
Articles; KAUST Catalysis Center (KCC); Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorSoufan, Othmanen
dc.contributor.authorBa Alawi, Wailen
dc.contributor.authorAfeef, Moataz A.en
dc.contributor.authorEssack, Magbubahen
dc.contributor.authorRodionov, Valentinen
dc.contributor.authorKalnis, Panosen
dc.contributor.authorBajic, Vladimir B.en
dc.date.accessioned2016-02-16T13:42:13Zen
dc.date.available2016-02-16T13:42:13Zen
dc.date.issued2015-12-14en
dc.identifier.citationMining Chemical Activity Status from High-Throughput Screening Assays 2015, 10 (12):e0144426 PLOS ONEen
dc.identifier.issn1932-6203en
dc.identifier.doi10.1371/journal.pone.0144426en
dc.identifier.urihttp://hdl.handle.net/10754/596363en
dc.description.abstractHigh-throughput screening (HTS) experiments provide a valuable resource that reports biological activity of numerous chemical compounds relative to their molecular targets. Building computational models that accurately predict such activity status (active vs. inactive) in specific assays is a challenging task given the large volume of data and frequently small proportion of active compounds relative to the inactive ones. We developed a method, DRAMOTE, to predict activity status of chemical compounds in HTP activity assays. For a class of HTP assays, our method achieves considerably better results than the current state-of-the-art-solutions. We achieved this by modification of a minority oversampling technique. To demonstrate that DRAMOTE is performing better than the other methods, we performed a comprehensive comparison analysis with several other methods and evaluated them on data from 11 PubChem assays through 1,350 experiments that involved approximately 500,000 interactions between chemicals and their target proteins. As an example of potential use, we applied DRAMOTE to develop robust models for predicting FDA approved drugs that have high probability to interact with the thyroid stimulating hormone receptor (TSHR) in humans. Our findings are further partially and indirectly supported by 3D docking results and literature information. The results based on approximately 500,000 interactions suggest that DRAMOTE has performed the best and that it can be used for developing robust virtual screening models. The datasets and implementation of all solutions are available as a MATLAB toolbox online at www.cbrc.kaust.edu.sa/dramote and can be found on Figshare.en
dc.language.isoenen
dc.publisherPublic Library of Science (PLoS)en
dc.relation.urlhttp://dx.plos.org/10.1371/journal.pone.0144426en
dc.rightsThis 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. http://creativecommons.org/licenses/by/4.0/en
dc.titleMining Chemical Activity Status from High-Throughput Screening Assaysen
dc.typeArticleen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.contributor.departmentKAUST Catalysis Center (KCC)en
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalPLoS ONEen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorSoufan, Othmanen
kaust.authorBa Alawi, Wailen
kaust.authorAfeef, Moataz A.en
kaust.authorEssack, Magbubahen
kaust.authorRodionov, Valentinen
kaust.authorKalnis, Panosen
kaust.authorBajic, Vladimir B.en
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