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 …

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

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

Regression modeling is becoming increasingly prevalent in organic chemistry as a tool for
reaction outcome prediction and mechanistic interrogation. Frequently, to acquire the …

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 …

Reaction databases provide a great deal of useful information to assist planning of
experiments but do not provide any interpretation or chemical concepts to accompany this …

Machine learning models are increasingly being utilized to predict outcomes of organic
chemical reactions. A large amount of reaction data is used to train these models, which is in …

Reaction condition recommendation is an essential element for the realization of computer-
assisted synthetic planning. Accurate suggestions of reaction conditions are required for …

Organic synthesis remains a major challenge in drug discovery. Although a plethora of
machine learning models have been proposed as solutions in the literature, they suffer from …

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 …