DeepSAT: Learning Molecular Structures from Nuclear Magnetic Resonance Data

Hyun Woo Kim, Chen Zhang, Raphael Reher, Mingxun Wang, Kelsey L. Alexander, Louis Félix Nothias, Yoo Kyong Han, Hyeji Shin, Ki Yong Lee, Kyu Hyeong Lee, Myeong Ji Kim, Pieter C. Dorrestein, William H. Gerwick, Garrison W. Cottrell

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

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 1H-13C 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.

Original languageEnglish
Article number71
JournalJournal of Cheminformatics
Volume15
Issue number1
DOIs
StatePublished - Dec 2023

Keywords

  • Convolutional neural network
  • Nuclear magnetic resonance
  • Structure prediction

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