Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …

Signal propagation in complex networks drives epidemics, is responsible for information
going viral, promotes trust and facilitates moral behavior in social groups, enables the …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Abstract Development of new products often relies on the discovery of novel molecules.
While conventional molecular design involves using human expertise to propose …

Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

Geometric deep learning (GDL) is based on neural network architectures that incorporate
and process symmetry information. GDL bears promise for molecular modelling applications …

Graphs are widely used as a popular representation of the network structure of connected
data. Graph data can be found in a broad spectrum of application domains such as social …

We study a fundamental problem in structure-based drug design---generating molecules
that bind to specific protein binding sites. While we have witnessed the great success of …

Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space,
perform particularly well in various tasks that process graph structure data. With the rapid …

Deep generative models of molecules have grown immensely in popularity, trained on
relevant datasets, these models are used to search through chemical space. The …