Networks—or graphs—are universal descriptors of systems of interacting elements. In
biomedicine and healthcare, they can represent, for example, molecular interactions …

Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …

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 …

Alloy modelling has a history of machine-learning-like approaches, preceding the tide of
data-science-inspired work. The dawn of computational databases has made the integration …

Deep learning, a branch of machine learning, is a frontier for artificial intelligence, aiming to
be closer to its primary goal—artificial intelligence. This paper mainly adopts the summary …

Message passing neural networks have become a method of choice for learning on graphs,
in particular the prediction of chemical properties and the acceleration of molecular …

In recent years, the use of machine learning (ML) in computational chemistry has enabled
numerous advances previously out of reach due to the computational complexity of …

A Gentle Introduction to Graph Neural Networks Distill About Prize Submit A Gentle Introduction
to Graph Neural Networks Neural networks have been adapted to leverage the structure and …

How to obtain informative representations of molecules is a crucial prerequisite in AI-driven
drug design and discovery. Recent researches abstract molecules as graphs and employ …

Deep learning bears promise for drug discovery, including advanced image analysis,
prediction of molecular structure and function, and automated generation of innovative …