Quantitative predictions of reaction properties, such as activation energy, have been limited
due to a lack of available training data. Such predictions would be useful for computer …

Accurate prediction of chemical reactions in solution is challenging for current state-of-the-
art approaches based on transition state modelling with density functional theory. Models …

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 …

The interplay of kinetics and thermodynamics governs reactive processes, and their control
is key in synthesis efforts. While sophisticated numerical methods for studying equilibrium …

We present a supervised learning approach to predict the products of organic reactions
given their reactants, reagents, and solvent (s). The prediction task is factored into two …

There is a lack of scalable quantitative measures of reactivity that cover the full range of
functional groups in organic chemistry, ranging from highly unreactive C–C bonds to highly …

Reaction predictor is an application for predicting chemical reactions and reaction pathways.
It uses deep learning to predict and rank elementary reactions by first identifying electron …

Quantum and classical reaction rate constant calculations come at the cost of exploring
potential energy surfaces. Due to the “curse of dimensionality”, their evaluation quickly …

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 …

Artificial intelligence is driving one of the most important revolutions in organic chemistry.
Multiple platforms, including tools for reaction prediction and synthesis planning based on …