DeepSAT: Learning Molecular Structures from Nuclear Magnetic Resonance Data.

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. Us

SUBMITTER: Kim HW 

PROVIDER: S-EPMC10406729 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature