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

Over the past decade, the landscape of molecular synthesis has gained major impetus by
the introduction of late-stage functionalization (LSF) methodologies. C–H functionalization …

Transition metal-catalysed C–H activation has emerged as an increasingly powerful platform
for molecular syntheses, enabling applications to natural product syntheses, late-stage …

The development of late-stage functionalization (LSF) methodologies, particularly C–H
functionalization, has revolutionized the field of organic synthesis. Over the past decade …

Artificial intelligence for graphs has achieved remarkable success in modelling complex
systems, ranging from dynamic networks in biology to interacting particle systems in physics …

Discovering new reactions, optimizing their performance, and extending the synthetically
accessible chemical space are critical drivers for major technological advances and more …

The field of predictive chemistry relates to the development of models able to describe how
molecules interact and react. It encompasses the long-standing task of computer-aided …

The progress of drug discovery and development is paced by milestones reached in organic
synthesis. In the last decade, the advent of late-stage functionalization (LSF) reactions has …

The borylation of aryl and heteroaryl C–H bonds is valuable for the site-selective
functionalization of C–H bonds in complex molecules. Iridium catalysts ligated by bipyridine …

Accurate and rapid evaluation of whether substrates can undergo the desired the
transformation is crucial and challenging for both human knowledge and computer …