Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Recent advances in computational modelling of atomic systems, spanning molecules,
proteins, and materials, represent them as geometric graphs with atoms embedded as …

Abstract Systems of interacting objects often evolve under the influence of underlying field
effects that govern their dynamics, yet previous works have abstracted away from such …

Denoising diffusion probabilistic models (DDPMs)(Ho et al.(2020)) have recently taken the
field of generative modeling by storm, pioneering new stateof-the-art results in disciplines …

We introduce ProteinWorkshop, a comprehensive benchmark suite for representation
learning on protein structures with Geometric Graph Neural Networks. We consider large …

Modeling the complex three-dimensional (3D) dynamics of relational systems is an
important problem in the natural sciences, with applications ranging from molecular …

Estimating the accuracy of protein structural models is a critical task in protein bioinformatics.
The need for robust methods in the estimation of protein model accuracy (EMA) is prevalent …

The quality prediction of quaternary structure models of a protein complex, in the absence of
its true structure, is known as the Estimation of Model Accuracy (EMA). EMA is useful for …

Generative deep learning methods have recently been proposed for generating 3D
molecules using equivariant graph neural networks (GNNs) within a denoising diffusion …

By conceiving physical systems as 3D many-body point clouds, geometric graph neural
networks (GNNs), such as SE (3)/E (3) equivalent GNNs, have showcased promising …