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

Exposure to environmental organic pollutants has triggered significant ecological impacts
and adverse health outcomes, which have been received substantial and increasing …

Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical
industry and research, where it has been utilized to efficiently identify new chemical entities …

Drinking-water quality is a rising concern in the United States (US), emphasizing the need to
broadly assess exposures and potential health effects at the point-of-use. Drinking-water …

Effective molecular representation learning is of great importance to facilitate molecular
property prediction. Recent advances for molecular representation learning have shown …

Deep learning on graphs has attracted significant interests recently. However, most of the
works have focused on (semi-) supervised learning, resulting in shortcomings including …

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 on graphs has recently achieved remarkable success on a variety of tasks,
while such success relies heavily on the massive and carefully labeled data. However …

Genome-wide association studies have identified risk loci linked to inflammatory bowel
disease (IBD)—a complex chronic inflammatory disorder of the gastrointestinal tract. The …

A diversity of chemicals are intentionally added to plastics to enhance their properties and
aid in manufacture. Yet the accumulated chemical composition of these materials is …