From Deep Learning to the Discovery of Promising VEGFR-2 Inhibitors.
ChemMedChem
; 19(16): e202400108, 2024 Aug 19.
Article
in En
| MEDLINE
| ID: mdl-38726553
ABSTRACT
Vascular 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±3.84â
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Vascular Endothelial Growth Factor Receptor-2
/
Protein Kinase Inhibitors
/
Cell Proliferation
/
Drug Discovery
/
Deep Learning
/
Antineoplastic Agents
Limits:
Humans
Language:
En
Journal:
ChemMedChem
/
ChemMedChem (Internet)
Journal subject:
FARMACOLOGIA
/
QUIMICA
Year:
2024
Type:
Article