Corpus ID: 204824294

Learning to Make Generalizable and Diverse Predictions for Retrosynthesis

@article{Chen2019LearningTM,
  title={Learning to Make Generalizable and Diverse Predictions for Retrosynthesis},
  author={Benson Chen and Tianxiao Shen and Tommi S. Jaakkola and Regina Barzilay},
  journal={ArXiv},
  year={2019},
  volume={abs/1910.09688}
}
  • Benson Chen, Tianxiao Shen, +1 author Regina Barzilay
  • Published 2019
  • Computer Science, Mathematics
  • ArXiv
  • We propose a new model for making generalizable and diverse retrosynthetic reaction predictions. Given a target compound, the task is to predict the likely chemical reactants to produce the target. This generative task can be framed as a sequence-to-sequence problem by using the SMILES representations of the molecules. Building on top of the popular Transformer architecture, we propose two novel pre-training methods that construct relevant auxiliary tasks (plausible reactions) for our problem… CONTINUE READING

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