论文标题
通过条件图逻辑网络的反归结预测
Retrosynthesis Prediction with Conditional Graph Logic Network
论文作者
论文摘要
逆合合成是有机化学中的基本问题之一。任务是识别可用于合成指定产品分子的反应物。最近，计算机辅助的反转合成正在发现化学和计算机科学社区的新兴趣。大多数现有方法都依赖于定义子图匹配规则的基于模板的模型，但是艰难的决策规则并不能定义化学反应是否可以进行。在这项工作中，我们使用条件图逻辑网络提出了一种新的方法来解决此任务，这是一个基于图形神经网络的条件图形模型，该模型何时应应用反应模板的规则，隐含地考虑结果反应是否在化学上是可行的和战略性的。我们还提出了有效的分层采样，以减轻计算成本。在基准数据集上的当前最新方法中，$ 8.1 \％$的显着提高$ 8.1 \％，但我们的模型还提供了预测的解释。
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.
