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Description is neutral and method-focused, emphasizing innovation in graph neural networks for binding energy prediction across folded and intrinsically disordered proteins, supported by benchmarking and experimental reference affinities, with no political, sensational, or ideological framing.
FlexENN is presented as a structure-aware GNN for predicting binding energies across folded and IDP interfaces, with benchmarking on MT tubulin-tubulin and MT-MAP systems, claiming improved energetic profiling over rigid-body methods.
Neutral; based on provided content.
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