Retrosynthesis is the process of recursively decomposing target molecules
into available building blocks. It plays an important role in solving problems
in organic synthesis planning. To automate or assist in the retrosynthesis
analysis, various retrosynthesis prediction algorithms have been proposed.
However, most of them are cumbersome and lack interpretability about their
predictions. In this paper, we devise a novel template-free algorithm for
automatic retrosynthetic expansion inspired by how chemists approach
retrosynthesis prediction. Our method disassembles retrosynthesis into two
steps: i) identify the potential reaction center of the target molecule through
a novel graph neural network and generate intermediate synthons, and ii)
generate the reactants associated with synthons via a robust reactant
generation model. While outperforming the state-of-the-art baselines by a
significant margin, our model also provides chemically reasonable
interpretation.

本文提出了一种基于图神经网络的模板自由算法，通过将逆向合成分为两个步骤来自动扩展化学反应，识别目标分子的潜在反应中心，生成中间合成物和生成与中间合成物相关的反应物。该方法在性能上超越了现有技术，并提供了合理的化学解释。