  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
provides results of dissatisfactory quality. In this study, we develop a
template-free self-corrected retrosynthesis predictor (SCROP) to perform a
retrosynthesis prediction task trained by using the Transformer neural network
architecture. In the method, the retrosynthesis planning is converted as a
machine translation problem between molecular linear notations of reactants and
the products. Coupled with a neural network-based syntax corrector, our method
achieves an accuracy of 59.0% on a standard benchmark dataset, which increases
>21% over other deep learning methods, and >6% over template-based methods.
More importantly, our method shows an accuracy 1.7 times higher than other
state-of-the-art methods for compounds not appearing in the training set.
