2026-10-19T00:27:23Zhttps://ebyu-rdlopen.rawdatalibrary.net/oai/request

oai:ebyu-rdlopen.rawdatalibrary.net:ebyu/110962026-10-18T21:13:17Zopenairedrivercom_ebyu_10000col_ebyu_10001

From Deep Learning to the Discovery of Promising VEGFR-2 InhibitorsYucel, Mehmet AliAdal, ErcanAktekin, Mine BugaHepokur, CeylanGambacorta, NicolaNicolotti, OrazioAlgul, OztekinBreast cancerDeep learningMolecular dockingVEGFRVirtual screeningAntineoplastic AgentsCell Line, TumorCell ProliferationDose-Response Relationship, DrugDrug DiscoveryDrug Screening Assays, AntitumorHumansMolecular Docking SimulationMolecular StructureProtein Kinase InhibitorsStructure-Activity RelationshipVascular Endothelial Growth Factor Receptor-21,3 bis(5 chloro 1h benzo[d]imidazol 2 yl)prop 2 en 1 onecisplatinea 11imatinibrhe 334unclassified drugvasculotropin inhibitorvasculotropin receptor 2antineoplastic agentKDR protein, humanprotein kinase inhibitorantiproliferative activityArticleVascular endothelial growth factor receptor 2 (VEGFR-2) stands as a prominent therapeutic target in oncology, playing a critical role in angiogenesis, tumor growth, and metastasis. FDA-approved VEGFR-2 inhibitors are associated with diverse side effects. Thus, finding novel and more effective inhibitors is of utmost importance. In this study, a deep learning (DL) classification model was first developed and then employed to select putative active VEGFR-2 inhibitors from an in-house chemical library including 187 druglike compounds. A pool of 18 promising candidates was shortlisted and screened against VEGFR-2 by using molecular docking. Finally, two compounds, RHE-334 and EA-11, were prioritized as promising VEGFR-2 inhibitors by employing PLATO, our target fishing and bioactivity prediction platform. Based on this rationale, we prepared RHE-334 and EA-11 and successfully tested their anti-proliferative potential against MCF-7 human breast cancer cells with IC50 values of 26.78±4.02 and 38.73±3.84 μM, respectively. Their toxicities were instead challenged against the WI-38. Interestingly, expression studies indicated that, in the presence of RHE-334, VEGFR-2 was equal to 0.52±0.03, thus comparable to imatinib equal to 0.63±0.03. In conclusion, this workflow based on theoretical and experimental approaches demonstrates effective in identifying VEGFR-2 inhibitors and can be easily adapted to other medicinal chemistry goals. © 2024 The Authors. ChemMedChem published by Wiley-VCH GmbH.John Wiley and Sons Ltd2024Articlehttps://rdlopen.rawdatalibrary.net/r/ebyu/11096https://doi.org/10.1002/cmdc.2024001081860-7179enChemMedCheminfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://ebyu-rdlopen.rawdatalibrary.net/tr/items/ebyu/11096