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Retrosynthesis Prediction using Grammar-based Neural Machine Translation: An Information-Theoretic Approach

preprint
submitted on 13.04.2021, 20:25 and posted on 15.04.2021, 07:21 by Vipul Mann, Venkat Venkatasubramanian
Retrosynthetic prediction is one of the main challenges in chemical synthesis that requires identifying reaction pathways and precursor molecules for synthesizing a target molecule. This requires a search over the space of plausible chemical reactions that often results in complex, multi-step, branched synthesis trees for even moderately complex organic reactions. Here, we propose an approach that performs single-step retrosynthesis prediction using SMILES grammar-based representations in a neural machine translation framework. Information-theoretic analyses of such grammar-representations reveal that they are both superior and well-suited for machine learning tasks due to their underlying redundancy and high information capacity compared to purely character-based representations. We report the top-1 prediction accuracy of 43.8% (top-5 measure of 61.4%) and syntactic validity of 95.6% (top-5 measure of 91.6%) on a standard reaction dataset. Comparing our model's performance with previous work that used purely character-based SMILES representations demonstrate improved accuracy and reduced grammatically invalid predictions.

Funding

Center for the Management of Systemic Risk (CMSR), Columbia University, New York

History

Email Address of Submitting Author

vm2583@columbia.edu

Institution

Columbia University

Country

United States

ORCID For Submitting Author

0000-0003-0225-8729

Declaration of Conflict of Interest

The authors declare no conflict of interest.

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