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Updated: Jun 26, 2025
A Simple Bioassay for the Evaluation of Vascular Endothelial Growth Factors
Published on: March 15, 2016
From Deep Learning to the Discovery of Promising VEGFR-2 Inhibitors
Mehmet Ali Yucel1, Ercan Adal2, Mine Buga Aktekin2
1Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Erzincan Binali Yildirim University, 24002, Erzincan, Türkiye.
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±3.84 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.
Insights
Researchers identified novel Vascular Endothelial Growth Factor Receptor 2 (VEGFR-2) inhibitors, RHE-334 and EA-11, using deep learning and molecular docking. These compounds show anti-proliferative effects against breast cancer cells.
Area of Science:
- Medicinal Chemistry
- Computational Biology
- Oncology
Background:
- Vascular Endothelial Growth Factor Receptor 2 (VEGFR-2) is crucial for tumor angiogenesis, growth, and metastasis.
- Existing VEGFR-2 inhibitors have side effects, necessitating the search for novel therapeutics.
Purpose of the Study:
- To identify novel and effective VEGFR-2 inhibitors using a combination of computational and experimental methods.
- To evaluate the anti-proliferative and toxicity profiles of identified compounds.
Main Methods:
- Development of a deep learning classification model to screen an in-house chemical library.
- Molecular docking to assess binding affinity of shortlisted compounds to VEGFR-2.
- In vitro anti-proliferative assays against MCF-7 breast cancer cells and toxicity assessment against WI-38 cells.
Main Results:
- Two compounds, RHE-334 and EA-11, were identified as potent VEGFR-2 inhibitors.
- RHE-334 and EA-11 demonstrated significant anti-proliferative activity against MCF-7 cells with IC50 values of 26.78±4.02 μM and 38.73±3.84 μM, respectively.
- RHE-334 showed VEGFR-2 inhibition comparable to imatinib.
Conclusions:
- The integrated workflow effectively identifies potential VEGFR-2 inhibitors.
- This approach can be adapted for other drug discovery and medicinal chemistry objectives.
- RHE-334 and EA-11 represent promising candidates for further development as anti-cancer agents.
