Buchwald-Hartwig coupling reaction is widely used in organic chemical synthesis, yield prediction is particularly important. In 2018, Science reported a yield prediction method based on random forest, but this method lacks feature learning. Therefore, an intelligent prediction and analysis method of coupling reaction yield based on deep forest is proposed. Combined with the advantages of deep learning and ensemble learning, the new deep model in the form of non-neural network is explored, which has good characterization learning ability and low difficulty in adjusting parameters, realizes the efficient prediction of chemical reaction, and analyzes the factors that have a significant impact on the prediction of reaction yield.
Buchwald-Hartwig amination reaction is widely applied in synthetic organic chemistry, which faces tedious and complex experimental process. In 2018, an interesting yield prediction technique is proposed via machine learning (random forest) in Science. However, the method is based on point prediction with many feature descriptors. For tackling these problems, complements and improvements have been made from the perspectives of machine learning and statistics, including feature dimensionality reduction, distributed prediction and visualization, so as to provide accurate and reliable decision information.
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