Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy†

@article{Schwaller2020PredictingRP,
  title={Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy†},
  author={P. Schwaller and Riccardo Petraglia and Valerio Zullo and Vishnu H. Nair and Rico H{\"a}uselmann and Riccardo Pisoni and C. Bekas and A. Iuliano and T. Laino},
  journal={Chemical Science},
  year={2020},
  volume={11},
  pages={3316 - 3325}
}
We present an extension of our Molecular Transformer model combined with a hyper-graph exploration strategy for automatic retrosynthesis route planning without human intervention. The single-step retrosynthetic model sets a new state of the art for predicting reactants as well as reagents, solvents and catalysts for each retrosynthetic step. We introduce four metrics (coverage, class diversity, round-trip accuracy and Jensen–Shannon divergence) to evaluate the single-step retrosynthetic models… Expand

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