Molecular machine learning (ML) underpins critical workflows in drug discovery, material science, and catalyst optimization ...

A graph represents every relationship as a dyad, or pairwise interaction. However, many complex systems can’t be represented by binary connections alone. Recent progress in the field shows how to move ...

The identification of drug-target Interactions (DTIs ... and input them into a hybrid mechanism of graph learning module to capture high-dimensional features in the latent space of drugs and ...

A review by researchers at Tongji University and the University of Technology Sydney published in Frontiers of Computer Science, highlights the powerful role of graph neural networks (GNNs) in ...