Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models

We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder-decoder architecture that consists of two recurrent neural networks, which has previously shown great success in solving other sequence-to-sequence prediction tasks such as machine translation. The model is trained on 50,000 experimental reaction examples from the United States patent literature, which span 10 broad reaction types that are commonly used by medicinal chemists. We find that our model performs comparably with a rule-based expert system baseline model, and also overcomes certain limitations…

• AC
  American Cancer Society

  Award: PF-15-007-01-CDD
• HF
  Hertz Foundation
• NI
  National Institute of General Medical Sciences

  Award: R01 GM062868
• NI
  National Institute of Allergy and Infectious Diseases

  Award: U19 AI109662
• DO
  Division of Chemistry

  Award: CHE1265956