203 citations · 389 across the 9 of their papers we have counts for
10 papers
Equivariant Graph Neural Networks for 3D Macromolecular Structure
Bowen Jing, Stephan Eismann, Pratham N. Soni +1
Representing and reasoning about 3D structures of macromolecules is emerging as a distinct challenge in machine learning. Here, we extend recent work on geometric vector perceptron…
Protein sequence-to-structure learning: Is this the end(-to-end revolution)?
Elodie Laine, Stephan Eismann, Arne Elofsson +1
The potential of deep learning has been recognized in the protein structure prediction community for some time, and became indisputable after CASP13. In CASP14, deep learning has b…
ATOM3D: Tasks On Molecules in Three Dimensions
Raphael J. L. Townshend, Martin Vögele, Patricia Suriana +10
Computational methods that operate on three-dimensional molecular structure have the potential to solve important questions in biology and chemistry. In particular, deep neural net…
Protein model quality assessment using rotation-equivariant, hierarchical neural networks
Stephan Eismann, Patricia Suriana, Bowen Jing +2
Proteins are miniature machines whose function depends on their three-dimensional (3D) structure. Determining this structure computationally remains an unsolved grand challenge. A…
Learning from Protein Structure with Geometric Vector Perceptrons
Bowen Jing, Stephan Eismann, Patricia Suriana +2
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…
Geometric Prediction: Moving Beyond Scalars
Raphael J. L. Townshend, Brent Townshend, Stephan Eismann +1
Many quantities we are interested in predicting are geometric tensors; we refer to this class of problems as geometric prediction. Attempts to perform geometric prediction in real-…