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

TL;DR

This paper introduces a deep learning approach using RNNs to identify the fragment structure of organic compounds from NMR data, demonstrating superior performance over traditional models.

Contribution

It presents a novel application of RNNs to NMR data analysis, with effective feature extraction methods and improved model generalization compared to SVM and KNN.

Findings

Peak sampling features outperform equidistant sampling.

RNN models show easier hyperparameter tuning.

RNN achieves better generalization than SVM and KNN.

Abstract

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.