Transition-metal complexes are attractive targets for the design of catalysts and functional
materials. The behavior of the metal–organic bond, while very tunable for achieving target …

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 …

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 …

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

The application of mechanistic generalizations is at the core of chemical reaction
development and application. These strategies are rooted in physical organic chemistry …

A Pd‐catalyzed asymmetric isomerization‐hydroamidocarbonylation of amide‐containing
alkenes was developed, affording a variety of chiral α‐alkyl succinimides in moderate to …

Exploration of novel, three‐dimensional chemical space is of growing interest in the drug
discovery community and with this comes the challenge for synthetic chemists to devise new …

Significant progress has been made in recent years in the use of AI and Machine Learning
(ML) for catalyst discovery and optimisation. The effectiveness of ML and data science …