Noise peak filtering in multi-dimensional NMR spectra using convolutional neural networks

Abstract

Motivation: Multi-dimensional NMR spectra are generally used for NMR signal assignment and structure analysis. There are several programs that can achieve highly automated NMR signal assignments and structure analysis. On the other hand, NMR spectra tend to have a large number of noise peaks even for data acquired with good sample and machine conditions, and it is still difficult to eliminate these noise peaks. Results: We have developed a method to eliminate noise peaks using convolutional neural networks, implemented in the program package Filt-Robot. The filtering accuracy of Filt-Robot was around 90-95% when applied to 2D and 3D NMR spectra, and the numbers of resulting non-noise peaks were close to those in corresponding manually prepared peaks lists. The filtering can strongly enhance automated NMR spectra analysis.