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

Models can codify our understanding of chemical reactivity and serve a useful purpose in
the development of new synthetic processes via, for example, evaluating hypothetical …

Burgeoning developments in machine learning (ML) and its rapidly growing adaptations in
chemistry are noteworthy. Motivated by the successful deployments of ML in the realm of …

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 …

Conspectus In the domain of reaction development, one aims to obtain higher efficacies as
measured in terms of yield and/or selectivities. During the empirical cycles, an admixture of …

Accurate prediction of reaction yield is the holy grail for computer-assisted synthesis
prediction, but current models have failed to generalize to large literature datasets. To …

Being able to predict the course of arbitrary chemical reactions is essential to the theory and
applications of organic chemistry. Approaches to the reaction prediction problems can be …

Assessing the outcomes of chemical reactions in a quantitative fashion has been a
cornerstone across all synthetic disciplines. Classically approached through empirical …

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 …

Machine Learning (ML) techniques face significant challenges when predicting advanced
chemical properties, such as yield, feasibility of chemical synthesis, and optimal reaction …