Artificial Neural Network and Multiple RegresAnalysis Applied to 2D-QSAR Studies: the Caof Imidazolidine-2,4-dione as PTP1B Inhibito

Basit Öğe Kaydı
dc.contributor.authorAggoun, Siham
dc.contributor.authorBelaidi, Salah
dc.contributor.authorGhamri, Meriam
dc.contributor.authorKerassa, Aicha
dc.contributor.authorCinar, Mehmet
dc.contributor.authorYamari, Imane
dc.contributor.authorAbchir, Oussama
dc.date.accessioned2024-10-04T18:58:37Z
dc.date.available2024-10-04T18:58:37Z
dc.date.issued2024
dc.departmentBayburt Üniversitesien_US
dc.description.abstractIn this investigation, we undertook a comprehensive quantitative structure-activity relationship (QSAR) analysis using both modern artificial neural network (ANN) methods and classical multiple linear regression methods (MLR). Our primary focus was on revealing the intricate connection between antidiabetic activity and the molecular structure of thirty-nine imidazolidine-2,4-dione derivatives. The B3LYP hybrid functional and 6-31G (d) basis set computed electronic properties at the quantum level. Rigorous benchmarking against experimental data validated the reliability of our quantum theory approach. Our statistical model effectively predicted activities closely aligned with experimental antidiabetic activities, quantified by the IC50 values. They also revealed a significant superiority of the ANN architecture (6-4-1) over the MLR method. This study stands as a meaningful contribution to the field, providing valuable insights for designing new antidiabetic drugs, particularly as potential inhibitors of tyrosine phosphate 1B (PTP1B). The explicit articulation of our primary aim and emphasis on the study’s significance underscore its potential impact on advancing drug development within the realm of antidiabetic therapeutics. © 2024, Department of Science and Technology. All rights reserved.en_US
dc.identifier.endpage346en_US
dc.identifier.issn0031-7683
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85186603532en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage333en_US
dc.identifier.urihttp://hdl.handle.net/20.500.12403/3913
dc.identifier.volume153en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherDepartment of Science and Technologyen_US
dc.relation.ispartofPhilippine Journal of Scienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subject(PTP1B)en_US
dc.subject4-dioneen_US
dc.subjectantidiabeticen_US
dc.subjectimidazolidine-2en_US
dc.subjectMLR-ANN modelen_US
dc.subjectQSARen_US
dc.titleArtificial Neural Network and Multiple RegresAnalysis Applied to 2D-QSAR Studies: the Caof Imidazolidine-2,4-dione as PTP1B Inhibitoen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu