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Found 7 papers, 2 papers with code
On the Fourier analysis in the SO(3) space : EquiLoPO Network
This work suggests the benefits of true rotational equivariance on SO(3) and flexible unconstrained filters enabled by the local activation function, providing a flexible framework for equivariant deep learning on volumetric data with potential applications across domains.
ILPO-NET: Network for the invariant recognition of arbitrary volumetric patterns in 3D
Effective recognition of spatial patterns and learning their hierarchy is crucial in modern spatial data analysis.
6DCNN with roto-translational convolution filters for volumetric data processing
In this work, we introduce 6D Convolutional Neural Network (6DCNN) designed to tackle the problem of detecting relative positions and orientations of local patterns when processing three-dimensional volumetric data.
Protein sequence-to-structure learning: Is this the end(-to-end revolution)?
The potential of deep learning has been recognized in the protein structure prediction community for some time, and became indisputable after CASP13.
Spherical convolutions on molecular graphs for protein model quality assessment
Within the framework of the protein model quality assessment problem, we demonstrate that the proposed spherical convolution method significantly improves the quality of model assessment compared to the standard message-passing approach.
Ranked #1 on
Protein Fold Quality Estimation
on CASP13 MQA
(Pearson Correlation Global metric)
Protein Folding Quality Prediction
Protein Fold Quality Estimation
DeepSymmetry : Using 3D convolutional networks for identification of tandem repeats and internal symmetries in protein structures
Our method is designed to identify tandem repeat proteins, proteins with internal symmetries, symmetries in the raw density maps, their symmetry order, and also the corresponding symmetry axes.
Deep convolutional networks for quality assessment of protein folds
Additional testing on decoys from the CASP12, CAMEO, and 3DRobot datasets confirms that the network performs consistently well across a variety of protein structures.
