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.
Intrinsic-extrinsic convolution and pooling for learning on 3d protein structures.arXiv preprint arXiv:2007.06252
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Secondary-structure-aware GNN using energy-filtered hydrogen-bond edges improves protein representation learning on standard benchmarks.
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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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Protein Representation Learning with Secondary-Structure and Energy-Filtered Hydrogen-Bond Graphs
Secondary-structure-aware GNN using energy-filtered hydrogen-bond edges improves protein representation learning on standard benchmarks.