  Reaction prediction remains one of the major challenges for organic
chemistry, and is a pre-requisite 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 re- actants, predicts the
likely products. We test this method on problems from a popular organic
chemistry textbook.
