Determining similarities of COVID-19-lung cancer drugs and affinity binding mode analysis by graph neural network-based GEFA method

Basit Öğe Kaydı
dc.authorid0000-0002-8470-4579en_US
dc.authorid0000-0002-3769-0071en_US
dc.authorid0000-0001-7538-262Xen_US
dc.contributor.authorBudak, Cafer
dc.contributor.authorMençik, Vasfiye
dc.contributor.authorGider, Veysel
dc.date.accessioned2021-12-28T06:25:27Z
dc.date.available2021-12-28T06:25:27Z
dc.date.issued2021en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Biyomedikal Mühendisliği Bölümüen_US
dc.descriptionWOS:000728704900001
dc.descriptionPMID: 34877907
dc.description.abstractCOVID-19 is a worldwide health crisis seriously endangering the arsenal of antiviral and antibiotic drugs. It is urgent to find an effective antiviral drug against pandemic caused by the severe acute respiratory syndrome (Sars-Cov-2), which increases global health concerns. As it can be expensive and time-consuming to develop specific antiviral drugs, reuse of FDA-approved drugs that provide an opportunity to rapidly distribute effective therapeutics can allow to provide treatments with known preclinical, pharmacokinetic, pharmacodynamic and toxicity profiles that can quickly enter in clinical trials. In this study, using the structural information of molecules and proteins, a list of repurposed drug candidates was prepared again with the graph neural network-based GEFA model. The data set from the public databases DrugBank and PubChem were used for analysis. Using the Tanimoto/jaccard similarity analysis, a list of similar drugs was prepared by comparing the drugs used in the treatment of COVID-19 with the drugs used in the treatment of other diseases. The resultant drugs were compared with the drugs used in lung cancer and repurposed drugs were obtained again by calculating the binding strength between a drug and a target. The kinase inhibitors (erlotinib, lapatinib, vandetanib, pazopanib, cediranib, dasatinib, linifanib and tozasertib) obtained from the study can be used as an alternative for the treatment of COVID-19, as a combination of blocking agents (gefitinib, osimertinib, fedratinib, baricitinib, imatinib, sunitinib and ponatinib) such as ABL2, ABL1, EGFR, AAK1, FLT3 and JAK1, or antiviral therapies (ribavirin, ritonavir-lopinavir and remdesivir).en_US
dc.identifier.citationBudak, C., Mençik, V. ve Gider, V. (2021). Determining similarities of COVID-19-lung cancer drugs and affinity binding mode analysis by graph neural network-based GEFA method. Journal of Biomolecular Structure & Dynamics, Early Access.en_US
dc.identifier.doi10.1080/07391102.2021.2010601
dc.identifier.issn0739-1102
dc.identifier.issn1538-0254
dc.identifier.pmid34877907
dc.identifier.scopus2-s2.0-85121384762
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/11468/8831
dc.identifier.volumeEarly Accessen_US
dc.identifier.wosWOS:000728704900001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorBudak, Cafer
dc.institutionauthorMençik, Vasfiye
dc.institutionauthorGider, Veysel
dc.language.isoenen_US
dc.publisherTaylor & Francisen_US
dc.relation.ispartofJournal of Biomolecular Structure & Dynamics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDrug similarityen_US
dc.subjectDrug repurposingen_US
dc.subjectGraph neural networken_US
dc.subjectKinase inhibitorsen_US
dc.subjectDrug affinityen_US
dc.subjectCOVID-19en_US
dc.titleDetermining similarities of COVID-19-lung cancer drugs and affinity binding mode analysis by graph neural network-based GEFA methoden_US
dc.titleDetermining similarities of COVID-19-lung cancer drugs and affinity binding mode analysis by graph neural network-based GEFA method
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
[ X ]
İsim:
Determining similarities of COVID-19 – lung cancer drugs and affinity binding mode analysis by graph neural network-based GEFA method.pdf
Boyut:
2.64 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Makale Dosyası
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
[ X ]
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Elektrik Elektronik Mühendisliği Bölümü Koleksiyonu
PubMed İndeksli Yayın Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu