5 citations · 10 across the 2 of their papers we have counts for
4 papers
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…
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-…
Hierarchical, rotation-equivariant neural networks to select structural models of protein complexes
Stephan Eismann, Raphael J. L. Townshend, Nathaniel Thomas +3
Predicting the structure of multi-protein complexes is a grand challenge in biochemistry, with major implications for basic science and drug discovery. Computational structure pred…
End-to-End Learning on 3D Protein Structure for Interface Prediction
Raphael J. L. Townshend, Rishi Bedi, Patricia A. Suriana +1
Despite an explosion in the number of experimentally determined, atomically detailed structures of biomolecules, many critical tasks in structural biology remain data-limited. Whet…