Journal
ACS CENTRAL SCIENCE
Volume 2, Issue 10, Pages 725-732Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acscentsci.6b00219
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Funding
- National Science Foundation [DGE-1144152]
- Samsung Advanced Institute of Technology
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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.
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