THE PREDICTION ACCURACY OF 1H AND 13C NMR CHEMICAL SHIFTS OF COUMARIN DERIVATIVES BY CHEMO/BIOINFORMATICS METHODS

Abstract

In plant biochemistry and physiology, coumarins are known as antioxidants, enzyme inhibitors and precursors of toxic substances. Nuclear magnetic resonance (NMR) spectra are primary sources of molecular structural data. NMR provides detailed information about the local environment of the atom which can be used to determine the atomic connectivity, stereochemistry, and molecular conformation. For many years the molecular structure has been determined by NMR spectroscopy and chemical shifts are determined manually with the help of computer programs. However, recent progress in computational chemistry and chemo/bioinformatics opened the possibility for the prediction of chemical shifts (especially those of 1H and 13C nuclei) of new chemicals. We analyzed the accuracy of three available chemoinformatics methods developed for the prediction of 1H and 13C chemical shifts based on deep neural networks CASCADE [1], an older prediction method based on classical neural networks NMRshiftDB [2,3], and group-contribution method in ChemDraw [4]. The mean absolute errors (MAEs) in the prediction of NMR shifts of four newly synthesized coumarins [5] by CASCADE, NMRshiftDB and ChemDraw are (respectively) 0.39, 0.65 and 0.32 ppm for 1H, and 1.5, 6.5 and 2.3 ppm for 13C atoms, shoving relatively big differences between these prediction methods.