In synthesis planning, it is important to determine suitable reaction conditions such that a chemical reaction proceeds as intended. Recent research attempts based on machine learning have proven to be effective in recommending reaction elements for specific categories regarding critical chemical context and operating conditions. However, existing methods can only make a single prediction per reaction and do not directly provide a complete specification of the reaction elements as the prediction. Therefore, their achievable performance is limited. In this study, we propose a generative modeling approach to predict multiple different reaction conditions for a chemical reaction, each of which fully specifies critical reaction elements such that these elements can be directly used as a feasible reaction condition. We formulate the problem of predicting reaction conditions as sampling from a generative distribution. We model the distribution by introducing a variational autoencoder augmented with a graph neural network and learn it from a reaction dataset. For a query reaction, multiple predictions can be obtained by repeated sampling from the distribution. Through experimental investigation on the reaction datasets of four major types of cross-coupling reactions, we demonstrate that the proposed method significantly outperforms existing methods in retrieving ground-truth reaction conditions.

中文翻译：

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预测化学反应的多种合适条件的生成模型

在合成计划中，重要的是确定合适的反应条件，以便化学反应按预期进行。最近基于机器学习的研究尝试已被证明在为特定类别的关键化学环境和操作条件推荐反应元素方面是有效的。然而，现有方法只能对每个反应进行单一预测，不能直接提供反应元素的完整规范作为预测。因此，它们可实现的性能是有限的。在这项研究中，我们提出了一种生成建模方法来预测化学反应的多个不同反应条件，每个反应条件都完全指定了关键反应元素，以便这些元素可以直接用作可行的反应条件。我们将预测反应条件的问题表述为从生成分布中抽样。我们通过引入一个用图形神经网络增强的变分自动编码器来对分布进行建模，并从反应数据集中学习它。对于查询反应，可以通过从分布中重复采样来获得多个预测。通过对四种主要类型的交叉偶联反应的反应数据集的实验研究，我们证明了所提出的方法在检索真实反应条件方面明显优于现有方法。通过从分布中重复采样可以获得多个预测。通过对四种主要类型的交叉偶联反应的反应数据集的实验研究，我们证明了所提出的方法在检索真实反应条件方面明显优于现有方法。通过从分布中重复采样可以获得多个预测。通过对四种主要类型的交叉偶联反应的反应数据集的实验研究，我们证明了所提出的方法在检索真实反应条件方面明显优于现有方法。