Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction

@article{Schwaller2019MolecularTA,
  title={Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction},
  author={P. Schwaller and T. Laino and T. Gaudin and P. Bolgar and C. Hunter and C. Bekas and Alpha A. Lee},
  journal={ACS Central Science},
  year={2019},
  volume={5},
  pages={1572 - 1583}
}
Organic synthesis is one of the key stumbling blocks in medicinal chemistry. [...] Key Method Molecular Transformer makes predictions by inferring the correlations between the presence and absence of chemical motifs in the reactant, reagent, and product present in the data set. Our model requires no handcrafted rules and accurately predicts subtle chemical transformations. Crucially, our model can accurately estimate its own uncertainty, with an uncertainty score that is 89% accurate in terms of classifying…Expand
Molecular Transformer unifies reaction prediction and retrosynthesis across pharma chemical space.
Prediction of chemical reaction yields using deep learning
Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits
Modern Hopfield Networks for Few- and Zero-Shot Reaction Prediction
Substructure-based neural machine translation for retrosynthetic prediction
Completion of partial reaction equations
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