Identifying molecular functional groups of organic compounds by deep learning of NMR data

We preprocess the raw nuclear magnetic resonance (NMR) spectrum and extract key features by using two different methodologies, called equidistant sampling and peak sampling for subsequent substructure pattern recognition. We also provide a strategy to address the imbalance issue frequently encountered in statistical modeling of NMR data set and establish two conventional support vector machine (SVM) and K-nearest neighbor (KNN) models to assess the capability of two feature selections, respectively. Our results in this study show that the models using the selected features of peak sampling outperform those using equidistant sampling. Then we build the recurrent neural network (RNN) model trained by data collected from peak sampling. Furthermore, we illustrate the easier optimization of hyperparameters and the better generalization ability of the RNN deep learning model by detailed comparison with traditional machine learning SVM and KNN models.

Automated summary

In this study, we preprocess the raw NMR spectrum and extract key features using two different methodologies. We establish conventional SVM and KNN models to evaluate the performance of feature selections. Our results demonstrate that the models using peak sampling outperform those using equidistant sampling. Furthermore, we build an RNN model trained with data collected from peak sampling and illustrate the advantages of RNN in terms of hyperparameter optimization and generalization ability through comparison with traditional machine learning models.