论文标题
学习曲面预测的图形模型
Learning Graph Models for Retrosynthesis Prediction
论文作者
论文摘要
逆合合成预测是有机合成中的一个基本问题，其中任务是识别可用于合成靶分子的前体分子。为此任务构建神经模型的关键考虑是将模型设计与化学家采用的策略保持一致。基于这个角度，本文介绍了一种基于图的方法，该方法利用了以下观点：前体分子的图形拓扑在化学反应过程中基本上没有改变。该模型首先预测图的集合将目标转换为称为合成子的不完整分子。接下来，该模型通过连接相关的离开组来学习将合成子扩展到完整分子中。这种分解简化了体系结构，使其预测更加可解释，并且也适合手动校正。我们的模型达到了$ 53.7 \％$的顶级1精度，表现优于以前的不含模板和基于半模板的方法。
Retrosynthesis prediction is a fundamental problem in organic synthesis, where the task is to identify precursor molecules that can be used to synthesize a target molecule. A key consideration in building neural models for this task is aligning model design with strategies adopted by chemists. Building on this viewpoint, this paper introduces a graph-based approach that capitalizes on the idea that the graph topology of precursor molecules is largely unaltered during a chemical reaction. The model first predicts the set of graph edits transforming the target into incomplete molecules called synthons. Next, the model learns to expand synthons into complete molecules by attaching relevant leaving groups. This decomposition simplifies the architecture, making its predictions more interpretable, and also amenable to manual correction. Our model achieves a top-1 accuracy of $53.7\%$, outperforming previous template-free and semi-template-based methods.
