Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models
journal contributionposted on 05.09.2017, 13:51 by Bowen Liu, Bharath Ramsundar, Prasad Kawthekar, Jade Shi, Joseph Gomes, Quang Luu Nguyen, Stephen Ho, Jack Sloane, Paul Wender, Vijay Pande
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 associated with rule-based expert systems and with any machine learning approach that contains a rule-based expert system component. Our model provides an important first step toward solving the challenging problem of computational retrosynthetic analysis.
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rule-based expert systemsmachine translationRetrosynthetic Reaction Predictionretrosynthetic reaction prediction taskrule-based expert system componentsequence-to-sequence prediction tasksreaction typesspan 10sequence-to-sequence mapping problemrule-based expert system baseline modelreaction examplesNeural Sequence-to-Sequence ModelsUnited States patent literatureretrosynthetic analysis