Transfer learning enables the molecular transformer to predict regio- and stereoselective reactions on carbohydrates

@article{Pesciullesi2020TransferLE,
  title={Transfer learning enables the molecular transformer to predict regio- and stereoselective reactions on carbohydrates},
  author={G. Pesciullesi and P. Schwaller and T. Laino and J. Reymond},
  journal={Nature Communications},
  year={2020},
  volume={11}
}
Organic synthesis methodology enables the synthesis of complex molecules and materials used in all fields of science and technology and represents a vast body of accumulated knowledge optimally suited for deep learning. While most organic reactions involve distinct functional groups and can readily be learned by deep learning models and chemists alike, regio- and stereoselective transformations are more challenging because their outcome also depends on functional group surroundings. Here, we… Expand

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