Predicting Retrosynthetic Reaction using Self-Corrected Transformer Neural Networks

@article{Zheng2020PredictingRR,
  title={Predicting Retrosynthetic Reaction using Self-Corrected Transformer Neural Networks},
  author={Shuangjia Zheng and Jiahua Rao and Zhongyue Zhang and Jun Xu and Yuedong Yang},
  journal={Journal of chemical information and modeling},
  year={2020}
}
Synthesis planning is the process of recursively decomposing target molecules into available precursors. Computer-aided retrosynthesis can potentially assist chemists in designing synthetic routes, but at present it is cumbersome and can't provide results of satisfactory qualities. In this study, we have developed a template-free self-corrected retrosynthesis predictor (SCROP) to predict retrosynthesis by using Transformer neural networks. In the method, the retrosynthesis planning was… 
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