Data augmentation and transfer learning strategies for reaction prediction in low chemical data regimes

@article{Zhang2021DataAA,
  title={Data augmentation and transfer learning strategies for reaction prediction in low chemical data regimes},
  author={Yun Zhang and Ling Wang and Xinqiao Wang and Chengyun Zhang and Jiamin Ge and Jing Tang and A. Su and H. Duan},
  journal={Organic chemistry frontiers},
  year={2021},
  volume={8},
  pages={1415-1423}
}
  • Yun Zhang, Ling Wang, +5 authors H. Duan
  • Published 2021
  • Chemistry
  • Organic chemistry frontiers
Effective and rapid deep learning method to predict chemical reactions contributes to the research and development of organic chemistry and drug discovery. Despite the outstanding capability of deep learning in retrosynthesis and forward synthesis, predictions based on small chemical datasets generally result in a low accuracy due to an insufficiency of reaction examples. Here, we introduce a new state-of-the-art method, which integrates transfer learning with the transformer model to predict… Expand
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