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Found 8 papers, 3 papers with code
A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications
Geometric graph is a special kind of graph with geometric features, which is vital to model many scientific problems.
Full-Atom Peptide Design with Geometric Latent Diffusion
Peptide design plays a pivotal role in therapeutics, allowing brand new possibility to leverage target binding sites that are previously undruggable.
Equivariant Pretrained Transformer for Unified Geometric Learning on Multi-Domain 3D Molecules
Pretraining on a large number of unlabeled 3D molecules has showcased superiority in various scientific applications.
Generalist Equivariant Transformer Towards 3D Molecular Interaction Learning
Extensive experiments on the interactions between proteins, small molecules and RNA/DNAs verify the effectiveness and generalization capability of our proposed method across different domains.
End-to-End Full-Atom Antibody Design
Finally, the updated antibody is docked to the epitope via the alignment of the shadow paratope.
Conditional Antibody Design as 3D Equivariant Graph Translation
Specifically, the relative improvement to baselines is about 23% in antigen-binding CDR design and 34% for affinity optimization.
Molecule Generation by Principal Subgraph Mining and Assembling
Molecule generation is central to a variety of applications.
Stylized Story Generation with Style-Guided Planning
Current storytelling systems focus more ongenerating stories with coherent plots regard-less of the narration style, which is impor-tant for controllable text generation.
