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  • Predicting Retrosynthetic Reactions Using Self-Corrected Transformer Neural Networks

    Author(s)
    Zheng, Shuangjia
    Rao, Jiahua
    Zhang, Zhongyue
    Xu, Jun
    Yang, Yuedong
    Griffith University Author(s)
    Yang, Yuedong
    Year published
    2020
    Metadata
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    Abstract
    Synthesis planning is the process of recursively decomposing target molecules into available precursors. Computer-aided retrosynthesis can potentially assist chemists in designing synthetic routes; however, at present, it is cumbersome and cannot provide satisfactory results. In this study, we have developed a template-free self-corrected retrosynthesis predictor (SCROP) to predict retrosynthesis using transformer neural networks. In the method, the retrosynthesis planning was converted to a machine translation problem from the products to molecular linear notations of the reactants. By coupling with a neural network-based ...
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    Synthesis planning is the process of recursively decomposing target molecules into available precursors. Computer-aided retrosynthesis can potentially assist chemists in designing synthetic routes; however, at present, it is cumbersome and cannot provide satisfactory results. In this study, we have developed a template-free self-corrected retrosynthesis predictor (SCROP) to predict retrosynthesis using transformer neural networks. In the method, the retrosynthesis planning was converted to a machine translation problem from the products to molecular linear notations of the reactants. By coupling with a neural network-based syntax corrector, our method achieved an accuracy of 59.0% on a standard benchmark data set, which outperformed other deep learning methods by >21% and template-based methods by >6%. More importantly, our method was 1.7 times more accurate than other state-of-the-art methods for compounds not appearing in the training set.
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    Journal Title
    Journal of Chemical Information and Modeling
    Volume
    60
    Issue
    1
    DOI
    https://doi.org/10.1021/acs.jcim.9b00949
    Subject
    Medicinal and biomolecular chemistry
    Theoretical and computational chemistry
    Science & Technology
    Life Sciences & Biomedicine
    Physical Sciences
    Technology
    Chemistry, Medicinal
    Publication URI
    http://hdl.handle.net/10072/393279
    Collection
    • Journal articles

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