论文标题
图形图形框架以进行回归合成预测
A Graph to Graphs Framework for Retrosynthesis Prediction
论文作者
论文摘要
计算化学中的一个基本问题是找到一组反应物来合成靶分子，又称反逆合合成预测。现有的最新方法依赖于将目标分子与大量反应模板相匹配，这些反应模板在计算上非常昂贵，并且遇到了覆盖范围的问题。在本文中，我们通过将目标分子图转换为一组反应物分子图，提出了一种称为G2GS的新型无模板方法。 G2GS首先通过识别反应中心将目标分子图拆分为一组合成子，然后通过变异图翻译框架将合成子转换为最终反应图。实验结果表明，G2GS明显优于现有的无模板方法，就TOP-1的准确性而言，最多可以超过63％，并且在基于最新模板的方法的表现接近的性能上，但不需要域知识，但不需要域知识，并且更可扩展。
A fundamental problem in computational chemistry is to find a set of reactants to synthesize a target molecule, a.k.a. retrosynthesis prediction. Existing state-of-the-art methods rely on matching the target molecule with a large set of reaction templates, which are very computationally expensive and also suffer from the problem of coverage. In this paper, we propose a novel template-free approach called G2Gs by transforming a target molecular graph into a set of reactant molecular graphs. G2Gs first splits the target molecular graph into a set of synthons by identifying the reaction centers, and then translates the synthons to the final reactant graphs via a variational graph translation framework. Experimental results show that G2Gs significantly outperforms existing template-free approaches by up to 63% in terms of the top-1 accuracy and achieves a performance close to that of state-of-the-art template based approaches, but does not require domain knowledge and is much more scalable.
