Being able to predict the course of arbitrary chemical reactions is essential to the theory and
applications of organic chemistry. Previous approaches are not high-throughput, are not …

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 …

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 …

Proposing reasonable mechanisms and predicting the course of chemical reactions is
important to the practice of organic chemistry. Approaches to reaction prediction have …

Machine learning (ML) and quantum mechanical (QM) methods can be used in two-way
synergy to build chemical reaction expert systems. The proposed ML approach identifies …

Predicting the course and major products of arbitrary reactions is a fundamental problem in
chemistry, one that chemists must address in a variety of tasks ranging from synthesis …

A learning model is proposed that predicts both products and reaction pathways by
combining machine learning and reaction network approaches. By training 50 fundamental …

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 …

One of the critical challenges in efficient synthesis route design is the accurate prediction of
chemical reactivity. Unlocking it could significantly facilitate chemical synthesis and hence …

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 …