Frustration Quenching and Network Topology of the Energy Landscape as Primary Determinants of Protein-Ligand Binding Pose Prediction by Deep Learning Models
Deep learning co-folding and docking models accurately predict ligand binding poses at orthosteric sites yet systematically underperform at allosteric pockets. Here, we demonstrate that this accuracy gap reflects a fundamental biophysical property of local energy landscape topology specifically, the magnitude of frustration quenching upon ligand binding rather than an intrinsic algorithmic…
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