Graph Neural Networks for Kinase–Inhibitor Affinity Prediction Using Docking Scores and Binding-Site Features

Kinases are major therapeutic targets, yet accurately predicting true inhibitor binding affinity remains a significant challenge. While molecular docking is useful for generating binding poses, docking scores alone often fail to capture the full complexity of kinase–inhibitor recognition. Current predictive models frequently overlook the spatial graph of the protein–ligand interface and the physicochemical characteristics of the binding site, limiting their effectiveness in compound ranking and