Machine Learning to Predict Diels–Alder Reaction Barriers from the Reactant State Electron Density
Machine Learning to Predict Diels–Alder Reaction Barriers from the Reactant State Electron Density
复制标题
机器学习根据反应物态电子密度预测 Diels-Alder 反应势垒
DOI：
10.1021/acs.jctc.1c00623
复制
发表时间：
2021
影响因子：
5.5
通讯作者：
Alexandrova, Anastassia N.
中科院分区：
文献类型：
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作者：
Vargas, Santiago;Hennefarth, Matthew R.;Liu, Zhihao;Alexandrova, Anastassia N.
反应势垒是我们理解化学反应和催化的关键。某些反应在化学上是如此开创性的，以至于无数的变种，无论有没有催化剂，都被研究过，它们的势垒已经被计算或实验测量。这些丰富的数据是利用机器学习模型的绝佳机会，机器学习模型可以快速预测障碍，而不需要明确的计算或测量。在这里，我们证明了反应态下量子力学电荷密度的拓扑描述符构成了一个既严谨又连续的集合，可以有效地用于高精度地预测反应势垒能。我们通过Diels-Alder反应证明了这一点，Diels-Alder反应在生物学和药物化学中非常重要，因此进行了广泛的研究。该反应的势垒范围高达270kJ/mol。虽然我们对溶液中更简单的Diels-Alder反应训练了我们的单目标监督(标记)回归算法，但它们也预测了在更复杂的环境中的反应障碍，例如由人工酶及其进化变体催化的Diels-Alder反应，这与墨迹猫的实验变化一致。我们希望该工具能广泛应用于溶液中或在催化剂存在下的各种反应，用于筛选和绕过繁琐的计算或实验。
Reaction barriers are key to our understanding of chemical reactivity and catalysis. Certain reactions are so seminal in chemistry that countless variants, with or without catalysts, have been studied, and their barriers have been computed or measured experimentally. This wealth of data represents a perfect opportunity to leverage machine learning models, which could quickly predict barriers without explicit calculations or measurement. Here, we show that the topological descriptors of the quantum mechanical charge density in the reactant state constitute a set that is both rigorous and continuous and can be used effectively for the prediction of reaction barrier energies to a high degree of accuracy. We demonstrate this on the Diels–Alder reaction, highly important in biology and medicinal chemistry, and as such, studied extensively. This reaction exhibits a range of barriers as large as 270 kJ/mol. While we trained our single-objective supervised (labeled) regression algorithms on simpler Diels–Alder reactions in solution, they predict reaction barriers also in significantly more complicated contexts, such a Diels–Alder reaction catalyzed by an artificial enzyme and its evolved variants, in agreement with experimental changes inkcat. We expect this tool to apply broadly to a variety of reactions in solution or in the presence of a catalyst, for screening and circumventing heavily involved computations or experiments.