361 citations · 954 across the 20 of their papers we have counts for
5 papers · 1 filter
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…
Rotation-Invariant Gait Identification with Quaternion Convolutional Neural Networks
Bowen Jing, Vinay Prabhu, Angela Gu +1
A desireable property of accelerometric gait-based identification systems is robustness to new device orientations presented by users during testing but unseen during the training…
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…