Recently, transformer architectures for graphs emerged as an alternative to established
techniques for machine learning with graphs, such as (message-passing) graph neural …

Applications of machine learning techniques for materials modeling typically involve
functions that are known to be equivariant or invariant to specific symmetries. While graph …

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

Generative AI is rapidly transforming the frontier of research in computational structural
biology. Indeed, recent successes have substantially advanced protein design and drug …

We present an exciting journey from 3D point‐cloud data (PCD) to the state of the art in
graph neural networks (GNNs) and their evolution with explainable artificial intelligence …

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 …

We consider representation learning for proteins with 3D structures. We build 3D graphs
based on protein structures and develop graph networks to learn their representations …

Injective multiset functions have a key role in the theoretical study of machine learning on
multisets and graphs. Yet, there remains a gap between the provably injective multiset …

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

Abstract Graph Neural Networks (GNNs) are often used for tasks involving the 3D geometry
of a given graph, such as molecular dynamics simulation. While incorporating Euclidean …