论文标题
翻新机：推动可解释的端到端逆转击变压器的限制
Retroformer: Pushing the Limits of Interpretable End-to-end Retrosynthesis Transformer
论文作者
论文摘要
反循环预测是有机合成中的基本挑战之一。任务是预测给定核心产品的反应物。随着机器学习的发展，计算机辅助的综合计划已越来越兴趣。提出了许多方法来解决此问题，以不同程度的依赖性对其他化学知识的依赖。在本文中，我们提出了Ratroformer，这是一种基于变压器的新型结构，用于逆合合成预测，而无需依赖任何用于分子编辑的化学信息学工具。通过提出的局部注意力头，该模型可以共同编码分子序列和图形，并在局部反应性区域和全局反应环境之间有效地交换信息。改造器达到了无端到端模板反折叠的新最新精度，并在更好的分子和反应有效性方面改善了许多强基础。此外，其生成过程是高度可解释和可控制的。总体而言，改造器推动了深层生成模型的反应推理能力的极限。
Retrosynthesis prediction is one of the fundamental challenges in organic synthesis. The task is to predict the reactants given a core product. With the advancement of machine learning, computer-aided synthesis planning has gained increasing interest. Numerous methods were proposed to solve this problem with different levels of dependency on additional chemical knowledge. In this paper, we propose Retroformer, a novel Transformer-based architecture for retrosynthesis prediction without relying on any cheminformatics tools for molecule editing. Via the proposed local attention head, the model can jointly encode the molecular sequence and graph, and efficiently exchange information between the local reactive region and the global reaction context. Retroformer reaches the new state-of-the-art accuracy for the end-to-end template-free retrosynthesis, and improves over many strong baselines on better molecule and reaction validity. In addition, its generative procedure is highly interpretable and controllable. Overall, Retroformer pushes the limits of the reaction reasoning ability of deep generative models.
