Automated Determination of the Molecular Substructure from Nuclear Magnetic Resonance Spectra Using Neural Networks.

Nuclear magnetic resonance (NMR) spectroscopy is an indispensable tool for determining the structural characteristics of a molecule by analyzing its chemical shifts. A wealth of NMR spectra therefore exists and continues to amass on a daily basis, at an ever-increasing rate owing to the progressive automation of chemical analysis. This growth and automation have led to the data analysis step in NMR spectroscopy becoming the main bottleneck in the structural characterization of a new chemical compound. In particular, the data interpretation step is slow and prone to error as it requires manual examination by a suitably trained scientist. Machine learning (ML) methods could overcome this bottleneck, pending that they can automatically correlate the collection of peaks in an NMR spectrum with the substructure of its subject molecule. This study explores the art of the possible using three types of ML methods that are based on neural-network architectures: a multilayer perceptron (MLP) + long short-term memory (LSTM) neural network, a convolutional neural network (CNN), and an MLP + recurrent neural network (RNN). NMR spectrum-structure correlations were encoded into each type of neural network using two forms of molecular representation, one employing functional groups and the other using a novel neighbor-based method. These models were trained on 34,503 and 17,311 experimental