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Found 5 papers, 1 papers with code
Deep Geometry Handling and Fragment-wise Molecular 3D Graph Generation
Most earlier 3D structure-based molecular generation approaches follow an atom-wise paradigm, incrementally adding atoms to a partially built molecular fragment within protein pockets.
Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge
Accurate prediction of protein-ligand binding structures, a task known as molecular docking is crucial for drug design but remains challenging.
Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction
To study this problem, we identify a Protein 3D Graph Structure Learning Problem for Robust Protein Property Prediction (PGSL-RP3), collect benchmark datasets, and present a protein Structure embedding Alignment Optimization framework (SAO) to mitigate the problem of structure embedding bias between the predicted and experimental protein structures.
Highly accurate and efficient deep learning paradigm for full-atom protein loop modeling with KarmaLoop
Protein loop modeling is the most challenging yet highly non-trivial task in protein structure prediction.
Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaboration
In this way, however, it is hard to generate realistic fragments with complicated structures.
