Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/1158
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dc.contributor.authorOnay, Aytun-
dc.contributor.authorOnay, Melih-
dc.contributor.authorAbul, Osman-
dc.date.accessioned2019-06-26T07:40:34Z
dc.date.available2019-06-26T07:40:34Z
dc.date.issued2017-04
dc.identifier.citationOnay, A., Onay, M., & Abul, O. (2017). Classification of nervous system withdrawn and approved drugs with ToxPrint features via machine learning strategies. Computer methods and programs in biomedicine, 142, 9-19.en_US
dc.identifier.issn0169-2607
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0169260716305247?via%3Dihub-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/1158-
dc.description.abstractBackground and objectives: Early-phase virtual screening of candidate drug molecules plays a key role in pharmaceutical industry from data mining and machine learning to prevent adverse effects of the drugs. Computational classification methods can distinguish approved drugs from withdrawn ones. We focused on 6 data sets including maximum 110 approved and 110 withdrawn drugs for all and nervous system diseases to distinguish approved drugs from withdrawn ones. Methods: In this study, we used support vector machines (SVMs) and ensemble methods (EMs) such as boosted and bagged trees to classify drugs into approved and withdrawn categories. Also, we used CORINA Symphony program to identify Toxprint chemotypes including over 700 predefined chemotypes for determination of risk and safety assesment of candidate drug molecules. In addition, we studied nervous system withdrawn drugs to determine the key fragments with The ParMol package including gSpan algorithm. Results: According to our results, the descriptors named as the number of total chemotypes and bond CN_amine_aliphatic_generic were more significant descriptors. The developed Medium Gaussian SVM model reached 78% prediction accuracy on test set for drug data set including all disease. Here, bagged tree and linear SVM models showed 89% of accuracies for phycholeptics and psychoanaleptics drugs. A set of discriminative fragments in nervous system withdrawn drug (NSWD) data sets was obtained. These fragments responsible for the drugs removed from market were benzene, toluene, N,N-dimethylethylamine, crotylamine, 5-methyl-2,4-heptadiene, octatriene and carbonyl group. Conclusion: This paper covers the development of computational classification methods to distinguish approved drugs from withdrawn ones. In addition, the results of this study indicated the identification of discriminative fragments is of significance to design a new nervous system approved drugs with interpretation of the structures of the NSWDs. (C) 2017 Elsevier B.V. All rights reserved.en_US
dc.language.isoenen_US
dc.publisherElsevier Ireland Ltd..en_US
dc.relation.ispartofComputer Methods And Programs in Biomedicineen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMachine Learningen_US
dc.subjectSupport Vector Machineen_US
dc.subjectDrug Discoveryen_US
dc.subjectToxprint Chemotypesen_US
dc.subjectApproved & Withdrawn Drugen_US
dc.titleClassification of Nervous System Withdrawn and Approved Drugs With Toxprint Features Via Machine Learning Strategiesen_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.volume142
dc.identifier.startpage9
dc.identifier.endpage19
dc.authorid0000-0002-9284-6112-
dc.identifier.wosWOS:000399509800003en_US
dc.identifier.scopus2-s2.0-85013168190en_US
dc.institutionauthorAbul, Osman-
dc.identifier.pmid28325450en_US
dc.identifier.doi10.1016/j.cmpb.2017.02.004-
dc.authorscopusid6602597612-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.relation.otherYuzuncu Yil University-BAPen_US
dc.identifier.scopusqualityQ1-
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.3. Department of Computer Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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