We present an extension of our Molecular Transformer model combined with a hyper-graph exploration strategy for automatic retrosynthesis route planning without human intervention. The single-step retrosynthetic model sets a new state of the art for predicting reactants as well as reagents, solvents and catalysts for each retrosynthetic step. We introduce four metrics (coverage, class diversity, round-trip accuracy and Jensen–Shannon divergence) to evaluate the single-step retrosynthetic models, using the forward prediction and a reaction classification model always based on the transformer architecture. The hypergraph is constructed on the fly, and the nodes are filtered and further expanded based on a Bayesian-like probability. We critically assessed the end-to-end framework with several retrosynthesis examples from literature and academic exams. Overall, the frameworks have an excellent performance with few weaknesses related to the training data. The use of the introduced metrics opens up the possibility to optimize entire retrosynthetic frameworks by focusing on the performance of the single-step model only.

中文翻译：

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使用基于变压器的模型和超图探索策略预测逆合成途径

我们提出了分子变压器模型的扩展，结合超图探索策略，用于自动逆合成路线规划，无需人工干预。单步逆合成模型为预测每个逆合成步骤的反应物以及试剂、溶剂和催化剂奠定了新的技术水平。我们引入四个指标（覆盖率、类别多样性、往返精度和 Jensen-Shannon 散度）来评估单步逆合成模型，使用前向预测和始终基于 Transformer 架构的反应分类模型。超图是动态构建的，节点根据类似贝叶斯的概率进行过滤和进一步扩展。我们通过文献和学术考试中的几个逆合成示例对端到端框架进行了批判性评估。总体而言，这些框架具有出色的性能，与训练数据相关的弱点很少。使用引入的指标开辟了通过仅关注单步模型的性能来优化整个逆向合成框架的可能性。