论文标题
$ \ mathsf {g^2retro} $作为retrosynthesis预测的两步图生成模型
$\mathsf{G^2Retro}$ as a Two-Step Graph Generative Models for Retrosynthesis Prediction
论文作者
论文摘要
逆合合成是一种过程，其中靶分子转化为潜在的反应物，因此可以鉴定出合成途径。最近，已经开发了计算方法来加速合成路线的设计。在本文中，我们为单步回曲局预测开发了一个生成框架$ \ mathsf {g^2retro} $。 $ \ mathsf {g^2retro} $模仿合成反应的反向逻辑。它首先预测靶分子中的反应中心（乘积），识别组装产物所需的合成子，并将这些合成子转化为反应物。 $ \ mathsf {g^2retro} $定义了一组全面的反应中心类型，并从产品的分子图中学习以预测潜在的反应中心。为了将合成子填充到反应物中，$ \ mathsf {g^2retro} $考虑了所有相关的合成结构和产品结构以识别最佳的完成路径，因此，将小型子结构顺序连接到合成子上。在这里，我们表明$ \ mathsf {g^2retro} $能够更好地预测基准数据集中给定产品的反应物，而不是最先进的方法。
Retrosynthesis is a procedure where a target molecule is transformed into potential reactants and thus the synthesis routes can be identified. Recently, computational approaches have been developed to accelerate the design of synthesis routes. In this paper, we develop a generative framework $\mathsf{G^2Retro}$ for one-step retrosynthesis prediction. $\mathsf{G^2Retro}$ imitates the reversed logic of synthetic reactions. It first predicts the reaction centers in the target molecules (products), identifies the synthons needed to assemble the products, and transforms these synthons into reactants. $\mathsf{G^2Retro}$ defines a comprehensive set of reaction center types, and learns from the molecular graphs of the products to predict potential reaction centers. To complete synthons into reactants, $\mathsf{G^2Retro}$ considers all the involved synthon structures and the product structures to identify the optimal completion paths, and accordingly attaches small substructures sequentially to the synthons. Here we show that $\mathsf{G^2Retro}$ is able to better predict the reactants for given products in the benchmark dataset than the state-of-the-art methods.
