3260 papers • 126 benchmarks • 313 datasets
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This work proposes to predict the ground-state 3D geometries from molecular graphs using machine learning methods using density functional theory (DFT), and implements two baseline methods that either predict the pairwise distance between atoms or atom coordinates in 3D space.
This work proposes a general framework, called hierarchical surface prediction (HSP), which facilitates prediction of high resolution voxel grids, and shows that high resolution predictions are more accurate than low resolution predictions.
A novel approach for implant generation based on a combination of 3D point cloud diffusion models and voxelization networks is proposed, which can propose an ensemble of different implants per defect, from which the physicians can choose the most suitable one.
Uni-Mol is a universal MRL framework that significantly enlarges the representation ability and application scope of MRL schemes, and achieves superior performance in 3D spatial tasks, including protein-ligand binding pose prediction, molecular conformation generation, etc.
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