Abstract Development of new products often relies on the discovery of novel molecules.
While conventional molecular design involves using human expertise to propose …

The development of new batteries has historically been achieved through discovery and
development cycles based on the intuition of the researcher, followed by experimental trial …

Research in chemistry increasingly requires interdisciplinary work prompted by, among
other things, advances in computing, machine learning, and artificial intelligence. Everyone …

Breakthroughs in molecular and materials discovery require meaningful outliers to be
identified in existing trends. As knowledge accumulates, the inherent bias of human intuition …

The reticular chemistry of metal–organic frameworks (MOFs) allows for the generation of an
almost boundless number of materials some of which can be a substitute for the traditionally …

Molecular optimization is a fundamental goal in the chemical sciences and is of central
interest to drug and material design. In recent years, significant progress has been made in …

Deep generative models are attracting much attention in the field of de novo molecule
design. Compared to traditional methods, deep generative models can be trained in a fully …

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad
applications to challenging tasks in chemistry and materials science. Examples include the …

Abstract Machine learning (ML) has enabled ground-breaking advances in the healthcare
and pharmaceutical sectors, from improvements in cancer diagnosis, to the identification of …

Graph neural networks (GNNs) have been widely used in molecular property prediction, but
explaining their black-box predictions is still a challenge. Most existing explanation methods …