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.
A new perspective on building efficient and expressive 3D equivariant graph neural networks, April 2023
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Drift-React produces full minimum energy pathways for reactions in a single step via SE(3) drifting fields, matching TS accuracy of iterative models with orders-of-magnitude speedup on Transition1x and Halo8 datasets.
citing papers explorer
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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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Drift-React: One-step Generation of Reaction Pathways via SE(3) Drifting Fields
Drift-React produces full minimum energy pathways for reactions in a single step via SE(3) drifting fields, matching TS accuracy of iterative models with orders-of-magnitude speedup on Transition1x and Halo8 datasets.