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