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

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

Applications of machine learning (ML) to synthetic chemistry rely on the assumption that
large numbers of literature-reported examples should enable construction of accurate and …

The molecules of today—the medicines that cure diseases, the agrochemicals that protect
our crops, the materials that make life convenient—are becoming increasingly sophisticated …

Electrooxidation has emerged as an increasingly viable platform in molecular syntheses that
can avoid stoichiometric chemical redox agents. Despite major progress in electrochemical …

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 …

As more data are introduced in the building of models of chemical reactivity, the mechanistic
component can be reduced until 'big data'applications are reached. These methods no …

Conspectus Numerous disciplines, such as image recognition and language translation,
have been revolutionized by using machine learning (ML) to leverage big data. In organic …

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

Application of machine learning (ML) to the prediction of reaction activation barriers is a new
and exciting field for these algorithms. The works covered here are specifically those in …