Journal Article

DeepSAT: Learning Molecular Structures from Nuclear Magnetic Resonance Data

Alexander, Kelsey, Kim, Hyun, Lee, Kyu, Zhang, Chen, Lee, Ki, Gerwick, William, Cottrell, Garrison, Shin, Hyeji, Kim, Myeong, Han, Yoo, Reher, Raphael, Nothias, Louis-Félix, Wang, Mingxun, Dorrestein, Pieter

2023

Overview

The identification of molecular structure is essential for understanding chemical diversity and for developing drug leads from small molecules. Nevertheless, the structure elucidation of small molecules by Nuclear Magnetic Resonance (NMR) experiments is often a long and non-trivial process that relies on years of training. To achieve this process efficiently, several spectral databases have been established to retrieve reference NMR spectra. However, the number of reference NMR spectra available is limited and has mostly facilitated annotation of commercially available derivatives. Here, we introduce DeepSAT, a neural network-based structure annotation and scaffold prediction system that directly extracts the chemical features associated with molecular structures from their NMR spectra. Using only the 1 H- 13 C HSQC spectrum, DeepSAT identifies related known compounds and thus efficiently assists in the identification of molecular structures. DeepSAT is expected to accelerate chemical and biomedical research by accelerating the identification of molecular structures.

• Publisher: Springer Science and Business Media LLC,Springer International Publishing,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Annotations / Artificial intelligence / Chemistry / Chemistry and Materials Science / Computational Biology/Bioinformatics / Computer Applications in Chemistry / Computer science / Convolutional neural network / Documentation and Information in Chemistry / Experiments / Information technology / Medical research / Metabolites / Molecular structure / Molecular weight / Natural products / Neural networks / NMR / Nuclear magnetic resonance / Pharmaceuticals / Pharmacy / QD1-999 / R&D / Research & development / Software / Spectra / Structure prediction / T58.5-58.64 / Theoretical and Computational Chemistry