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

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.