Neural networks for the prediction organic chemistry reactions
This addresses the problem of synthetic planning for organic chemists, but appears incremental as it builds on existing methods like neural networks and SMARTS.
The paper tackles the challenge of predicting organic chemistry reactions by developing a neural network system that uses a new reaction fingerprinting method and SMARTS transformations to predict likely products from reagents and reactants, achieving testing on problems from a popular textbook.
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.