Machine Learning (ML) and Molecular Dynamics-Driven Optimization of VEGFR2 Ligands against Hepatocellular Carcinoma.

The marginally higher median MW and logP in the active class, while adhering to the general principles of Lipinski’s Rule of Five, indicate a slight trend toward larger and more lipophilic molecules being more potent.

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This is highly significant because, despite employing the DFG-out pocket (3VO3) as a template, our ML/MD pipeline successfully identified a potent hinge-binder, demonstrating its capacity to select compounds with mechanistically novel profiles.

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