Retrosynthesis is one of the fundamental problems in organic chemistry. The
task is to identify reactants that can be used to synthesize a specified
product molecule. Recently, computer-aided retrosynthesis is finding renewed
interest from both chemistry and computer science communities. Most existing
approaches rely on template-based models that define subgraph matching rules,
but whether or not a chemical reaction can proceed is not defined by hard
decision rules. In this work, we propose a new approach to this task using the
Conditional Graph Logic Network, a conditional graphical model built upon graph
neural networks that learns when rules from reaction templates should be
applied, implicitly considering whether the resulting reaction would be both
chemically feasible and strategic. We also propose an efficient hierarchical
sampling to alleviate the computation cost. While achieving a significant
improvement of $8.1\%$ over current state-of-the-art methods on the benchmark
dataset, our model also offers interpretations for the prediction.

本文提出了一种新的基于条件图逻辑网络的化学反应逆向设计方法，使用图神经网络隐式考虑反应是否在化学上可行和策略性，并提出了一种有效的分层采样，成功将当前现有方法的性能提高了 8.1%。