From Deep Learning to the Discovery of Promising VEGFR-2 Inhibitors

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dc.contributor.authorYucel, Mehmet Ali
dc.contributor.authorAdal, Ercan
dc.contributor.authorAktekin, Mine Buga
dc.contributor.authorHepokur, Ceylan
dc.contributor.authorGambacorta, Nicola
dc.contributor.authorNicolotti, Orazio
dc.contributor.authorAlgul, Oztekin
dc.date.accessioned2024-10-26T18:09:43Z
dc.date.available2024-10-26T18:09:43Z
dc.date.issued2024
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description.abstractVascular 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 mu M, 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. Cancer research aims for safer VEGFR-2 inhibitors. Using deep learning, we identified two promising candidates, RHE-334 and EA-11, prioritized through molecular docking and PLATO platform. In MCF-7 cells, RHE-334 showed significant anti-proliferative potential, comparable to imatinib. This study offers a novel approach for VEGFR-2 inhibition, demonstrating its adaptability to other medicinal chemistry pursuits. image
dc.identifier.doi10.1002/cmdc.202400108
dc.identifier.issn1860-7179
dc.identifier.issn1860-7187
dc.identifier.issue16
dc.identifier.pmid38726553
dc.identifier.scopus2-s2.0-85196367355
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1002/cmdc.202400108
dc.identifier.urihttps://hdl.handle.net/20.500.12418/30252
dc.identifier.volume19
dc.identifier.wosWOS:001250647200001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherWiley-V C H Verlag Gmbh
dc.relation.ispartofChemmedchem
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectDeep learning
dc.subjectVEGFR
dc.subjectVirtual screening
dc.subjectMolecular docking
dc.subjectBreast cancer
dc.titleFrom Deep Learning to the Discovery of Promising VEGFR-2 Inhibitors
dc.typeArticle

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