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Predicting Retrosynthetic Pathways Using a Combined Linguistic Model and Hyper-Graph Exploration Strategy

preprint
submitted on 17.10.2019 and posted on 21.10.2019 by Philippe Schwaller, Riccardo Petraglia, Valerio Zullo, Vishnu H Nair, Rico Andreas Haeuselmann, Riccardo Pisoni, Costas Bekas, Anna Iuliano, Teodoro Laino

We present an extension of our Molecular Transformer architecture combined with a hyper-graph exploration strategy for automatic retrosyn- thesis route planning without human intervention. The single-step ret- rosynthetic model sets a new state of the art for predicting reactants as well as reagents, solvents and catalysts for each retrosynthetic step. We introduce new metrics (coverage, class diversity, round-trip accuracy and Jensen-Shannon divergence) to evaluate the single-step retrosynthetic models, using the forward prediction and a reaction classification model always based on the transformer architecture. The hypergraph is con- structed on the fly, and the nodes are filtered and further expanded based on a Bayesian-like probability. We critically assessed the end-to-end framework with several retrosynthesis examples from literature and aca- demic exams. Overall, the frameworks has a very good performance with few weaknesses due to the bias induced during the training process. The use of the newly introduced metrics opens up the possibility to optimize entire retrosynthetic frameworks through focusing on the performance of the single-step model only.


Available on IBM RXN for Chemistry: https://rxn.res.ibm.com.

History

Email Address of Submitting Author

phs@zurich.ibm.com

Institution

IBM Research - Zurich

Country

Switzerland

ORCID For Submitting Author

0000-0003-3046-6576

Declaration of Conflict of Interest

No conflict of interest.

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