VEGFR2 inhibitors( King Saud University)

Vascular Endothelial Growth Factor Receptor 2 (VEGFR2) is a critical therapeutic target in cancer due to its role in pathological angiogenesis and tumor progression. Despite available FDA-approved VEGFR2 tyrosine kinase inhibitors (TKIs), challenges like resistance, off-target toxicities, and low discovery success rates persist, necessitating more efficient and scalable approaches to identify novel inhibitors. We trained five machine learning (ML) and two deep learning (DL) models on a dataset of 17,750 compounds derived from three public databases (e.g., PubChem, ChEMBL, and BindingDB). The best-performing model, Support Vector Machine (SVM) using an ECFP-like fingerprint (91.7% test accuracy), was used to select top-predicted compounds for subsequent ADMET analysis and molecular docking. We also introduce a custom ML/DL program predicting VEGFR2 inhibition probability based on SMILES input. Models demonstrated strong performance, achieving an average Matthews Correlation Coefficient (MCC) of 0.775; ECFP and RDKit fingerprints yielded the highest predictive performance. ADMET analysis indicated favorable pharmacokinetic properties (e.g., good passive GI absorption, low BBB permeation) for 11 of 17 top-ranked novel small molecules, suitable for oral peripheral administration. Molecular docking confirmed strong binding affinities to the VEGFR2 ATP-binding site (ranging -7.9 to -11.4 kcal/mol) for all 17 candidates. Notably, molecule 4 (-11.4 kcal/mol) and molecule 8 (-11.3 kcal/mol) exhibited affinities superior to FDA-approved inhibitors Sorafenib and Ponatinib. Molecular dynamics simulation results (structural and thermodynamics analysis) confirm these data. These findings identify promising novel VEGFR2 inhibitors with favorable drug-like properties. To facilitate the identification of potential VEGFR2 kinase inhibitors, we developed VEGFR2pred, a user-friendly graphical interface that integrates pretrained machine learning models, including RF, SVM, and XGB, and two best-performing molecular representations based on our results: ECFP and RDKit. To enhance accessibility and reproducibility, the complete tool, along with source code and installation instructions, has been made publicly available on GitHub.

This study aimed to assess the ability of diosgenin and its derivatives to suppress three angiogenic receptor tyrosine kinases-VEGFR2, FGFR1, and PDGFRA-through comprehensive in silico screening, molecular docking, and molecular dynamics simulations.

We screened 1,525 plant-derived compounds, 20 sapogenins, and three diosgenin derivatives for drug-likeness and bioavailability using SwissADME. The top candidates were docked against the three receptor tyrosine kinases using PyRx. Diosgenin and 14 of its derivatives were then further analyzed. Molecular dynamics simulations were performed using NAMD3 with CHARMM force fields to assess the stability of the protein-ligand complexes. Parameters such as RMSD, RMSF, Rg, and ΔG were evaluated.

Diosgenin was among the top 10 hits for all three receptor tyrosine kinases. It showed the strongest binding and stable interactions with VEGFR2 (ΔG:  -11.03 kcal/mol) compared to lenvatinib (ΔG: -7.52 kcal/mol) and sorafenib (ΔG: -4.01 kcal/mol). The FGFR1-diosgenin and PDGFRA-Diosgenin complexes displayed positive ΔG values, indicating less favorable thermodynamic binding. Three diosgenin derivatives (Formosanin C, Dioscin, and 2-Amin-5-(4-pyridyl)-1,3,4-thiadiazole DG-8d moiety) were also evaluated for their anticancer potential. While Formosanin C showed the highest binding affinities among all three derivatives, diosgenin uniquely interacted with the key catalytic residues of VEGFR2, suggesting functionally more relevant inhibition despite slightly lower docking scores. Similarly, Yamogenin, another sapogenin evaluated, exhibited a high binding affinity across all three angiogenic receptors. Although Yamogenin showed high affinity across all three receptors, molecular dynamics confirmed the superior stability of diosgenin with VEGFR2 (ΔG = -11.03 vs. -9.85 kcal/mol).

Diosgenin represents a promising and selective VEGFR2 inhibitor with  potential to have anti-angiogenic therapeutic activity.