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This paper proposes CPES, a curvature-informed potential energy surface graph neural network for protein-ligand binding affinity prediction. It integrates physics-informed curvature representations to model conformational flexibility and achieves improved predictive performance on benchmark datasets.
Introduces InteractBind, a large-scale dataset and benchmark for fine-grained evaluation of protein-ligand models, focusing on binding-site localization and non-covalent interaction prediction. Evaluates eight existing models and finds limited binding-site localization despite strong binary binding prediction.