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We preprocess the raw NMR spectrum and extract key characteristic features byusing two different methodologies, called equidistant sampling and peaksampling for subsequent substructure pattern recognition; meanwhile may providethe alternative strategy to address the imbalance issue of the NMR datasetfrequently encountered in dataset collection of statistical modeling andestablish two conventional SVM and KNN models to assess the capability of twofeature selection, respectively. Our results in this study show that the modelsusing the selected features of peak sampling outperform the ones using theother. Then we build the Recurrent Neural Network (RNN) model trained by Data Bcollected from peak sampling. Furthermore, we illustrate the easieroptimization of hyper parameters and the better generalization ability of theRNN deep learning model by comparison with traditional machine learning SVM andKNN models in detail.

Identifying the fragment structure of the organic compounds by deeply learning the original NMR data