Search Results for author:
Found 9 papers, 3 papers with code
Equivariant Graph Neural Networks for 3D Macromolecular Structure
Representing and reasoning about 3D structures of macromolecules is emerging as a distinct challenge in machine learning.
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.
ATOM3D: Tasks On Molecules in Three Dimensions
We implement several classes of three-dimensional molecular learning methods for each of these tasks and show that they consistently improve performance relative to methods based on one- and two-dimensional representations.
Protein model quality assessment using rotation-equivariant, hierarchical neural networks
Proteins are miniature machines whose function depends on their three-dimensional (3D) structure.
Learning from Protein Structure with Geometric Vector Perceptrons
Learning on 3D structures of large biomolecules is emerging as a distinct area in machine learning, but there has yet to emerge a unifying network architecture that simultaneously leverages the graph-structured and geometric aspects of the problem domain.
Geometric Prediction: Moving Beyond Scalars
This novel and data-efficient ability to predict real-world geometric tensors opens the door to addressing many problems through the lens of geometric prediction, in areas such as 3D vision, robotics, and molecular and structural biology.
Hierarchical, rotation-equivariant neural networks to select structural models of protein complexes
Predicting the structure of multi-protein complexes is a grand challenge in biochemistry, with major implications for basic science and drug discovery.
Learning Neural PDE Solvers with Convergence Guarantees
Partial differential equations (PDEs) are widely used across the physical and computational sciences.
Shape optimization in laminar flow with a label-guided variational autoencoder
Computational design optimization in fluid dynamics usually requires to solve non-linear partial differential equations numerically.
