Representation learning on biomolecular structures using equivariant graph attention
Learning and reasoning about 3D molecular structures with varying size is an emerging and
important challenge in machine learning and especially in the development of …
important challenge in machine learning and especially in the development of …
Equivariant graph attention networks for molecular property prediction
Learning and reasoning about 3D molecular structures with varying size is an emerging and
important challenge in machine learning and especially in drug discovery. Equivariant …
important challenge in machine learning and especially in drug discovery. Equivariant …
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. Here, we extend recent work on geometric vector …
distinct challenge in machine learning. Here, we extend recent work on geometric vector …
Geometry-complete perceptron networks for 3d molecular graphs
A Morehead, J Cheng - Bioinformatics, 2024 - academic.oup.com
Motivation The field of geometric deep learning has recently had a profound impact on
several scientific domains such as protein structure prediction and design, leading to …
several scientific domains such as protein structure prediction and design, leading to …
ViSNet: an equivariant geometry-enhanced graph neural network with vector-scalar interactive message passing for molecules
Geometric deep learning has been revolutionizing the molecular modeling field. Despite the
state-of-the-art neural network models are approaching ab initio accuracy for molecular …
state-of-the-art neural network models are approaching ab initio accuracy for molecular …
Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing
Geometric deep learning has been revolutionizing the molecular modeling field. Despite the
state-of-the-art neural network models are approaching ab initio accuracy for molecular …
state-of-the-art neural network models are approaching ab initio accuracy for molecular …
A new perspective on building efficient and expressive 3D equivariant graph neural networks
Geometric deep learning enables the encoding of physical symmetries in modeling 3D
objects. Despite rapid progress in encoding 3D symmetries into Graph Neural Networks …
objects. Despite rapid progress in encoding 3D symmetries into Graph Neural Networks …
Graph neural network with local frame for molecular potential energy surface
Modeling molecular potential energy surface is of pivotal importance in science. Graph
Neural Networks have shown great success in this field. However, their message passing …
Neural Networks have shown great success in this field. However, their message passing …
Improving Equivariant Graph Neural Networks on Large Geometric Graphs via Virtual Nodes Learning
Y Zhang, J Cen, J Han, Z Zhang, J Zhou… - Forty-first International … - openreview.net
Equivariant Graph Neural Networks (GNNs) have made remarkable success in a variety of
scientific applications. However, existing equivariant GNNs encounter the efficiency issue for …
scientific applications. However, existing equivariant GNNs encounter the efficiency issue for …
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 …
learning, but there has yet to emerge a unifying network architecture that simultaneously …