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)
Year published
2020
Metadata
Show full item recordAbstract
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 ...
View more >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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View more >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.
View less >
Journal Title
Journal of Chemical Information and Modeling
Volume
60
Issue
1
Subject
Medicinal and biomolecular chemistry
Theoretical and computational chemistry
Science & Technology
Life Sciences & Biomedicine
Physical Sciences
Technology
Chemistry, Medicinal