Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …

Large language models (LLMs) have emerged as powerful tools in chemistry, significantly
impacting molecule design, property prediction, and synthesis optimization. This review …

Map** molecular structure to odor perception is a key challenge in olfaction. We used
graph neural networks to generate a principal odor map (POM) that preserves perceptual …

Abstract Artificial Neural Networks (ANNs), sometimes also called models for deep learning,
are used extensively for the prediction of a range of environmental variables. While the …

In the past decade, we have witnessed the rise of deep learning to dominate the field of
artificial intelligence. Advances in artificial neural networks alongside corresponding …

Background: Spatial transcriptomics technologies fully utilize spatial location information,
tissue morphological features, and transcriptional profiles. Integrating these data can greatly …

Graph neural networks (GNNs) are a powerful architecture for tackling graph learning tasks,
yet have been shown to be oblivious to eminent substructures such as cycles. We present …

In population genetics, the emergence of large-scale genomic data for various species and
populations has provided new opportunities to understand the evolutionary forces that drive …

Mass spectrometry is a vital tool in the analytical chemist's toolkit, commonly used to identify
the presence of known compounds and elucidate unknown chemical structures. All of these …

This article is one of two Distill publications about graph neural networks. Take a look at A
Gentle Introduction to Graph Neural Networks for a companion view on many things graph …