基于注意驱动卷积神经网络的偶联反应产率回归预测

IF 2.9 2区 化学 Q2 CHEMISTRY, MULTIDISCIPLINARY Match-Communications in Mathematical and in Computer Chemistry Pub Date : 2022-08-01 DOI:10.46793/match.89-1.199h
Hexun Hou, Hengzhe Wang, Yanhui Guo, Puyu Zhang, Lichao Peng, Xiaohui Yang
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引用次数: 0

摘要

传统的提高Buchwald-Hartwig交叉偶联反应产率的方法是改变反应物或反应条件,但该反应存在反应条件苛刻、合成路线复杂等问题。2018年,Doyle在《科学》杂志上报道了一种基于随机森林的产量预测方法。而随机森林中回归树的预测值是叶节点目标变量的平均值,对特征同等重要。我们将重点放在重要的特征信息上,以获得更准确的良率预测值。因此,将一些先进的深度学习方法应用于化学反应的性能预测是很有意义的,在此过程中可能需要较少的训练数据。
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Regression Prediction of Coupling Reaction Yield Based on Attention-Driven Convolutional Neural Network
The traditional method to improve the yield of Buchwald-Hartwig cross coupling reaction is to change the reactants or reaction conditions, but the reaction has many problems, such as harsh reaction conditions, complex synthetic route. In 2018, Doyle reported a yield prediction method based on random forest in Science. However, the predicted value of the regression tree in the random forest is the average value of the target variable of the leaf node, which treats the feature as equally important. We focused on the important characteristic information in order to obtain a more accurate yield prediction value. Therefore, it is of interest to apply some advanced deep learning methods to the performance prediction of chemical reactions, during which less training data may be required.
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来源期刊
CiteScore
4.40
自引率
26.90%
发文量
71
审稿时长
2 months
期刊介绍: MATCH Communications in Mathematical and in Computer Chemistry publishes papers of original research as well as reviews on chemically important mathematical results and non-routine applications of mathematical techniques to chemical problems. A paper acceptable for publication must contain non-trivial mathematics or communicate non-routine computer-based procedures AND have a clear connection to chemistry. Papers are published without any processing or publication charge.
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