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

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Abstract

We preprocess the raw 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 modelling of NMR data set and establish two conventional SVM and 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 hyper parameters and the better generalization ability of the RNN deep learning model by detailed comparison with traditional machine learning SVM and KNN models.This article is protected by copyright. All rights reserved.