Neural Networks for the Prediction of Organic Chemistry Reactions

Jennifer N. Wei, David Duvenaud, Alan Aspuru-Guzik

Abstract:

Reaction prediction remains one of the major
challenges for organic chemistry and is a prerequisite for
efficient synthetic planning. It is desirable to develop
algorithms that, like humans, “learn” from being exposed to
examples of the application of the rules of organic chemistry.
We explore the use of neural networks for predicting reaction
types, using a new reaction fingerprinting method. We
combine this predictor with SMARTS transformations to
build a system which, given a set of reagents and reactants,
predicts the likely products. We test this method on problems
from a popular organic chemistry textbook.