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 …

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

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

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 …

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 …

The material basis of a sustainable society will depend on chemical products and processes
that are designed following principles that make them conducive to life. Important inherent …

The data sets available to train models for in silico drug discovery efforts are often small.
Indeed, the sparse availability of labeled data is a major barrier to artificial-intelligence …

Activity and property prediction models are the central workhorses in drug discovery and
materials sciences, but currently, they have to be trained or fine-tuned for new tasks. Without …