We present a supervised learning approach to predict the products of organic reactions given their reactants, reagents, and solvent(s). The prediction task is factored into two stages comparable to manual expert approaches: considering possible sites of reactivity and evaluating their relative likelihoods. By training on hundreds of thousands of reaction precedents covering a broad range of reaction types from the patent literature, the neural model makes informed predictions of chemical reactivity. The model predicts the major product correctly over 85% of the time requiring around 100 ms per example, a significantly higher accuracy than achieved by previous machine learning approaches, and performs on par with expert chemists with years of formal training. We gain additional insight into predictions via the design of the neural model, revealing an understanding of chemistry qualitatively consistent with manual approaches.

中文翻译：

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用于化学反应性预测的图卷积神经网络模型†

我们提出了一种有监督的学习方法来预测有机反应在给定反应物，试剂和溶剂的情况下的产物。与手工专家方法相比，预测任务分为两个阶段：考虑可能的反应位点和评估其相对可能性。通过训练涵盖专利文献中广泛反应类型的数十万种反应先例，神经模型对化学反应性做出了明智的预测。该模型可以正确预测主要产品超过85％的时间，每个示例大约需要100毫秒，这比以前的机器学习方法要准确得多，并且可以与经过多年正式培训的专业化学家相媲美。我们通过以下方式获得对预测的更多见解 神经模型的设计，从本质上揭示了对化学方法的理解与手工方法的一致性。