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 …

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 …

Catalyst discovery is increasingly relying on computational chemistry, and many of the
computational tools are currently being automated. The state of this automation and the …

The dawn of the 21st century has brought with it a surge of research related to computer-
guided approaches to catalyst design. In the past two decades, chemoinformatics, the …

The application of computational methods in enantioselective catalysis has evolved from the
rationalization of the observed stereochemical outcome to their prediction and application to …

The lack of publicly available, large, and unbiased datasets is a key bottleneck for the
application of machine learning (ML) methods in synthetic chemistry. Data from electronic …

The development of computational tools to support organic synthesis, including the
prediction of reaction pathways, optimization and selectivity, is a topic of intense current …

Modern, enantioselective catalyst development is driven largely by empiricism. Although this
approach has fostered the introduction of most of the existing synthetic methods, it is …

Impressive progress in computational asymmetric catalysis has been made in the past
twenty years owing to advancements in algorithm and method development for predicting …

Abstract Enzymes offering chemo-, regio-, and stereoselectivity enable the asymmetric
synthesis of high-value chiral molecules. Unfortunately, the drawback that naturally …