h-MINT improves ligand-protein binding affinity prediction by 2-4% and virtual screening metrics by 1-3% via overlapping fragment tokenization and hierarchical modeling.
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GAUC selects coresets in pre-trained VLM embedding space by jointly optimizing distributional fidelity via MMD, prompt robustness via effective mutual information difference, and output stability via predictive variance penalty.
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h-MINT: Modeling Pocket-Ligand Binding with Hierarchical Molecular Interaction Network
h-MINT improves ligand-protein binding affinity prediction by 2-4% and virtual screening metrics by 1-3% via overlapping fragment tokenization and hierarchical modeling.
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Geometry-Aware Uncertainty Coresets for Robust Visual In-Context Learning in Histopathology
GAUC selects coresets in pre-trained VLM embedding space by jointly optimizing distributional fidelity via MMD, prompt robustness via effective mutual information difference, and output stability via predictive variance penalty.