NMR signal processing, prediction, and structure verification with machine learning techniques.

Area of Science:

• Analytical Chemistry
• Computational Chemistry
• Spectroscopy

Background:

• Machine learning (ML) has a long history in Nuclear Magnetic Resonance (NMR) spectroscopy.
• Recent advancements in deep learning (DL), coupled with increased data and computational power, have accelerated ML applications in NMR.
• ML/DL algorithms are versatile, excelling in classification, regression, clustering, and dimensionality reduction for large datasets.

Purpose of the Study:

• To review the diverse applications of ML and DL in NMR signal processing.
• To explore the use of ML/DL in the analysis of small molecules using NMR data.
• To highlight ML/DL's role in automatic structure verification and prediction of NMR observables.

Main Methods:

• Focus on ML/DL techniques applied to NMR data.
• Analysis of NMR signal processing methodologies.
• Examination of small molecule analysis strategies within NMR.

Main Results:

• ML/DL methods show significant success in various NMR applications like metabonomics, clinical diagnosis, and relaxometry.
• These techniques are effectively utilized for processing and analyzing NMR signals from small molecules.
• Demonstrated utility in automated structure verification and prediction of NMR spectral properties.

Conclusions:

• ML and DL are powerful tools transforming NMR data analysis.
• The application of ML/DL in NMR signal processing and small molecule analysis is rapidly expanding.
• Future potential lies in further integration for enhanced structure elucidation and property prediction in NMR.