Machine Learning (ML) and Molecular Dynamics-Driven Optimization of VEGFR2 Ligands against Hepatocellular Carcinoma
Farzana Yasmeen1, Abdul Manan1, Wook Kim1
1Department of Molecular Science and Technology, Ajou University, Suwon, Republic of Korea.
Objectives:
Vascular endothelial growth factor receptor 2 (VEGFR2) is a critical therapeutic target in hepatocellular carcinoma (HCC) due to its role in angiogenesis and tumor progression. While several inhibitors are currently used, clinical utility is often limited by resistance and adverse effects, necessitating the discovery of novel therapeutic agents. The aim of this study was to identify and characterize novel, highly effective VEGFR2 inhibitors using an integrated computational pipeline to advance the development of new HCC treatments.
Methods:
A comprehensive dataset from the ChEMBL database was curated and standardized for Quantitative Structure-Activity Relationship (QSAR) modeling. A binary classification framework was employed, where a Light Gradient Boosting Machine (LGBM) model demonstrated superior predictive performance. Two lead compounds and a reference were selected for in-depth molecular modeling. Their binding poses were predicted via molecular docking and subsequently subjected to 200 ns Molecular Dynamics (MD) simulations to assess stability and conformational dynamics. Thermodynamic binding affinities were calculated using the Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) method.
Results:
The LGBM model achieved high accuracy and a robust Matthews Correlation Coefficient (MCC) on an independent test set. MD analysis, including Root Mean Square Deviation (RMSD) and Radius of Gyration (Rg), confirmed stable binding throughout the 200 ns trajectory. MMPBSA calculations validated the binding affinities, identifying van der Waals and electrostatic interactions as the primary driving forces for complex stability.
Conclusion:
This study successfully bridges machine learning with advanced molecular simulations, offering a validated workflow for the rational design and optimization of novel small-molecule VEGFR2 inhibitors.
Insights
This study identifies novel, effective vascular endothelial growth factor receptor 2 (VEGFR2) inhibitors for hepatocellular carcinoma (HCC) using machine learning and molecular simulations. The validated computational approach accelerates the development of new targeted cancer therapies.
Area of Science:
- Computational chemistry and cheminformatics
- Drug discovery and medicinal chemistry
- Oncology and cancer research
Background:
- Vascular endothelial growth factor receptor 2 (VEGFR2) is a key target in hepatocellular carcinoma (HCC) due to its role in tumor angiogenesis.
- Existing VEGFR2 inhibitors face challenges with resistance and adverse effects, driving the need for novel therapeutic agents.
- Development of new treatments for HCC is critical.
Purpose of the Study:
- To identify and characterize novel, highly effective VEGFR2 inhibitors for HCC treatment.
- To utilize an integrated computational pipeline combining machine learning and molecular simulations.
- To advance the rational design of small-molecule inhibitors targeting VEGFR2.
Main Methods:
- Curated ChEMBL database for Quantitative Structure-Activity Relationship (QSAR) modeling.
- Employed a Light Gradient Boosting Machine (LGBM) model for predictive analysis.
- Conducted molecular docking, 200 ns Molecular Dynamics (MD) simulations, and MMPBSA calculations for binding affinity and stability assessment.
Main Results:
- The LGBM model demonstrated high accuracy and a robust Matthews Correlation Coefficient (MCC).
- MD simulations confirmed stable binding of lead compounds throughout the 200 ns trajectory.
- MMPBSA calculations validated binding affinities, highlighting van der Waals and electrostatic interactions.
Conclusions:
- Successfully integrated machine learning with molecular simulations for rational drug design.
- Validated a computational workflow for optimizing novel small-molecule VEGFR2 inhibitors.
- This approach offers a promising strategy for developing new HCC therapies.
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