PRIME is a five-level hierarchical equivariant graph model for proteins that uses physics-informed deterministic operators to exchange information across scales and achieves state-of-the-art results on fold classification and reaction class prediction.
Schnet–a deep learning architecture for molecules and materials.The Journal of chemical physics, 148(24)
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EnFlow integrates flow-based conformer generation with energy landscape modeling to enable joint ensemble generation and ground-state identification using only 1-2 ODE steps.
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PRIME: Protein Representation via Physics-Informed Multiscale Equivariant Hierarchies
PRIME is a five-level hierarchical equivariant graph model for proteins that uses physics-informed deterministic operators to exchange information across scales and achieves state-of-the-art results on fold classification and reaction class prediction.
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Energy-Guided Generative Modeling for Low-Energy Molecular Structure Discovery
EnFlow integrates flow-based conformer generation with energy landscape modeling to enable joint ensemble generation and ground-state identification using only 1-2 ODE steps.