Using Machine Learning To Predict Suitable Conditions for Organic Reactions,ACS Central Science

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Using Machine Learning To Predict Suitable Conditions for Organic Reactions
ACS Central Science ( IF 12.7 ) Pub Date : 2018-11-16 00:00:00 , DOI: 10.1021/acscentsci.8b00357
Hanyu Gao ₁ , Thomas J Struble ₁ , Connor W Coley ₁ , Yuran Wang ₁ , William H Green ₁ , Klavs F Jensen ₁

Affiliation

Reaction condition recommendation is an essential element for the realization of computer-assisted synthetic planning. Accurate suggestions of reaction conditions are required for experimental validation and can have a significant effect on the success or failure of an attempted transformation. However, de novo condition recommendation remains a challenging and under-explored problem and relies heavily on chemists’ knowledge and experience. In this work, we develop a neural-network model to predict the chemical context (catalyst(s), solvent(s), reagent(s)), as well as the temperature most suitable for any particular organic reaction. Trained on ∼10 million examples from Reaxys, the model is able to propose conditions where a close match to the recorded catalyst, solvent, and reagent is found within the top-10 predictions 69.6% of the time, with top-10 accuracies for individual species reaching 80–90%. Temperature is accurately predicted within ±20 °C from the recorded temperature in 60–70% of test cases, with higher accuracy for cases with correct chemical context predictions. The utility of the model is illustrated through several examples spanning a range of common reaction classes. We also demonstrate that the model implicitly learns a continuous numerical embedding of solvent and reagent species that captures their functional similarity.

中文翻译：

使用机器学习来预测有机反应的合适条件

反应条件推荐是实现计算机辅助合成规划的基本要素。实验验证需要准确的反应条件建议，并且可能对尝试转化的成功或失败产生重大影响。然而，从头条件推荐仍然是一个具有挑战性且尚未充分探索的问题，并且在很大程度上依赖于化学家的知识和经验。在这项工作中，我们开发了一个神经网络模型来预测化学环境（催化剂、溶剂、试剂）以及最适合任何特定有机反应的温度。该模型基于 Reaxys 的约 1000 万个示例进行训练，能够提出在 69.6% 的时间内在前 10 名预测中发现与记录的催化剂、溶剂和试剂密切匹配的条件，并且单个预测的准确度排名前 10 名。品种达到80%~90%。在 60-70% 的测试案例中，温度预测准确度与记录温度相差±20°C，对于具有正确化学背景预测的案例，预测准确度更高。通过涵盖一系列常见反应类别的几个示例说明了该模型的实用性。我们还证明，该模型隐式地学习了溶剂和试剂种类的连续数值嵌入，以捕获它们的功能相似性。

更新日期：2018-11-16

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