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.

通过使用 Transformer 神经网络架构的自校正回溯合成预测器 (SCROP)，我们将回溯合成规划转化为反应物和产物的分子线性符号之间的机器翻译问题，并结合基于神经网络的语法校正器，在标准基准数据集上实现了 59% 的准确性，比其他深度学习方法提高了 21% 以上，比基于模板的方法提高了 6% 以上。最重要的是，对于训练集中未出现的化合物，我们方法的准确性比其他最先进的方法提高了 1.7 倍。