Quantum Mechanics and Machine Learning Synergies: Graph Attention Neural Networks to Predict Chemical Reactivity
Quantum Mechanics and Machine Learning Synergies: Graph Attention Neural Networks to Predict Chemical Reactivity
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DOI：
10.1021/acs.jcim.1c01400
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发表时间：
2021-03
影响因子：
5.6
通讯作者：
Mohammadamin Tavakoli;Aaron Mood;D. V. Vranken;P. Baldi
中科院分区：
文献类型：
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作者：
Mohammadamin Tavakoli;Aaron Mood;D. V. Vranken;P. Baldi
目前缺乏涵盖有机化学中所有官能团（从高度不反应的C-C键到高度反应的裸离子）的可扩展的反应性定量测量方法。实验测量反应性既昂贵又耗时，而且没有一种方法具有足够的动态范围来覆盖化学反应性空间的天文数字大小。在之前的量子化学研究中，我们引入了甲基阳离子亲和（MCA*）和甲基阴离子亲和（MAA*），使用溶剂化模型，作为有机官能团在最广泛范围内反应性的定量测量。虽然MCA*和MAA*提供了很好的反应性参数估计，但通过密度泛函理论（DFT）模拟计算它们是耗时的。为了解决这个问题，我们首先使用DFT计算了2400多个有机分子的MCA*和MAA*，从而建立了一个大型的化学反应性评分数据集。然后，我们设计了深度学习方法来预测分子结构的反应性，并使用这些精心整理的数据集与分子结构的不同表示相结合来训练它们。通过10倍交叉验证，我们发现将图注意神经网络应用于分子结构的关系模型可以产生最准确的反应性估计，在预测MCA*±3.0或MAA*±3.0时，测试精度超过91%，超过50个数量级。最后，我们展示了这些反应性评分在两个任务中的应用：(1)化学反应预测和(2)反应机制的组合生成。MCA*和MAA*分数的策划数据集可通过ChemDB化学信息学门户网站cdb.ics.uci.edu在化学反应数据集下获得。
There is a lack of scalable quantitative measures of reactivity that cover the full range of functional groups in organic chemistry, ranging from highly unreactive C-C bonds to highly reactive naked ions. Measuring reactivity experimentally is costly and time-consuming, and no single method has sufficient dynamic range to cover the astronomical size of chemical reactivity space. In previous quantum chemistry studies, we have introduced Methyl Cation Affinities (MCA*) and Methyl Anion Affinities (MAA*), using a solvation model, as quantitative measures of reactivity for organic functional groups over the broadest range. Although MCA* and MAA* offer good estimates of reactivity parameters, their calculation through Density Functional Theory (DFT) simulations is time-consuming. To circumvent this problem, we first use DFT to calculate MCA* and MAA* for more than 2,400 organic molecules thereby establishing a large data set of chemical reactivity scores. We then design deep learning methods to predict the reactivity of molecular structures and train them using this curated data set in combination with different representations of molecular structures. Using 10-fold cross-validation, we show that graph attention neural networks applied to a relational model of molecular structures produce the most accurate estimates of reactivity, achieving over 91% test accuracy for predicting the MCA* ± 3.0 or MAA* ± 3.0, over 50 orders of magnitude. Finally, we demonstrate the application of these reactivity scores to two tasks: (1) chemical reaction prediction and (2) combinatorial generation of reaction mechanisms. The curated data sets of MCA* and MAA* scores is available through the ChemDB chemoinformatics web portal at cdb.ics.uci.edu under Chemical Reactivities data sets.