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Combining structure-based pharmacophore modeling and machine learning for the identification of novel BTK inhibitors

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dc.contributor.author이덕철-
dc.contributor.author동재준-
dc.date.accessioned2022-12-22T05:18:46Z-
dc.date.available2022-12-22T05:18:46Z-
dc.date.issued2022-12-
dc.identifier.issn0141-8130-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/192360-
dc.description.abstractBruton's tyrosine kinase (BTK) is a critical enzyme which is involved in multiple signaling pathways that regulate cellular survival, activation, and proliferation, making it a major cancer therapeutic target. We applied the novel integrated structure-based pharmacophore modeling, machine learning, and other in silico studies to screen the Korean chemical database (KCB) to identify the potential BTK inhibitors (BTKi). Further evaluation of these inhibitors on three different human cancer cell lines showed significant cell growth inhibitory activity. Among the 13 compounds shortlisted, four demonstrated consistent cell inhibition activity among breast, gastric, and lung cancer cells (IC50 below 3 μM). The selected compounds also showed significant kinase inhibition activity (IC50 below 5 μM). The current study suggests the potential of these inhibitors for targeting BTK malignant tumors.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherElsevier-
dc.relation.isPartOfINTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.subject.MESHAgammaglobulinaemia Tyrosine Kinase-
dc.subject.MESHHumans-
dc.subject.MESHMachine Learning-
dc.subject.MESHPhosphorylation-
dc.subject.MESHProtein Kinase Inhibitors* / chemistry-
dc.subject.MESHProtein Kinase Inhibitors* / pharmacology-
dc.subject.MESHProtein-Tyrosine Kinases* / metabolism-
dc.titleCombining structure-based pharmacophore modeling and machine learning for the identification of novel BTK inhibitors-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Family Medicine (가정의학교실)-
dc.contributor.googleauthorTanuj Sharma-
dc.contributor.googleauthorVenu Venkatarame Gowda Saralamma-
dc.contributor.googleauthorDuk Chul Lee-
dc.contributor.googleauthorMohammad Azhar Imran-
dc.contributor.googleauthorJaehyuk Choi-
dc.contributor.googleauthorMohammad Hassan Baig-
dc.contributor.googleauthorJae-June Dong-
dc.identifier.doi10.1016/j.ijbiomac.2022.09.151-
dc.contributor.localIdA02716-
dc.contributor.localIdA04927-
dc.relation.journalcodeJ03456-
dc.identifier.eissn1879-0003-
dc.identifier.pmid36130643-
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0141813022020827?via%3Dihub-
dc.subject.keywordBruton's tyrosine kinase-
dc.subject.keywordMachine learning-
dc.subject.keywordPharmacophore-
dc.subject.keywordVirtual screening-
dc.contributor.alternativeNameLee, Duk Chul-
dc.contributor.affiliatedAuthor이덕철-
dc.contributor.affiliatedAuthor동재준-
dc.citation.volume222-
dc.citation.numberPart A-
dc.citation.startPage239-
dc.citation.endPage250-
dc.identifier.bibliographicCitationINTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES, Vol.222(Part A) : 239-250, 2022-12-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Family Medicine (가정의학교실) > 1. Journal Papers

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