Document Type : Guest editorial


Medicinal Chemistry and Natural Products Research Group, School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, James Parsons Building, Byrom Street, Liverpool L3 3AF, UK


Plants produce a variety of chemical compounds, and plants have been the main source of new chemical entities and novel chemical scaffolds or templates, unfolding new challenges for organic synthetic chemists to explore appropriate synthetic routes for their total synthesis. Because of the unique chemical diversity offered by plants, it is often a tedious and complicated process when it comes to structure elucidation of phytochemicals. Recent advances in spectroscopic techniques, particularly in NMR and MS methodologies, have provided various tools that assist phytochemists with the structure elucidation of known or new phytochemicals. However, spectroscopic data interpretation manually requires significant experience and expertise, knowledge, intellectual ability and patience; often the manual process can be quite time consuming and even be frustrating. Over the last several decades, especially with the phenomenal progress in computation and applications of artificial intelligence and various mathematical modelling, several automated spectral data interpretation and structure elucidation software have become available to the phytochemists. These automated tools, not necessarily have replaced human intelligence or efforts, but certainly have facilitated the process, and improved the accuracy of structure elucidation of phytochemicals.


Anker, L. S., Jurs, P. C., 1992. Prediction of carbon-13 nuclear magnetic resonance chemical shifts by artificial neural networks. Anal. Chem. 64, 1157-1164.
Blinov, K. A., Elyashberg, M. E., Molodtsov, S. G., Williams, A. J., Martirosian, E. R., 2001. An expert system for automated structure elucidation utilizing 1H-1H, 13C-1H and 15N-1H 2D NMR correlations. Fresenius J. Anal. Chem. 369, 709–714.
Doucet, J. P., Panaye, A., Feuilleaubois, E., Ladd, P., 1993. Neural networks and 13C NMR shift prediction. J. Chem. Inf. Comput. Sci. 33, 320-324.
Elyashberg, M., Blinov, K., Molodtsov, S., Smurnyy, Y., Williamn, A. J., Churanova, T., 2009. Computer-assisted methods for molecular structure elucidation: realizing a spectroscopist's dream. J. Cheminformatics 1, 3.
Funatsu, K., Susuta, Y., Sasaki, S-I., 1989. Application of the automated structure elucidation system (CHEMICS) to the chemistry of natural products. Pure & Appl. Chem. 61(3), 609-612.
Furst, A., Pretsch, E., Robien, W., 1990, Comprehensive parameter set for the prediction of the 13C NMR chemical shifts of sp3-hybridized carbon atoms in organic compounds. Anal. Chim. Acta 233, 213-222.
Golotvin, S. S., Vodopianov, E., Pol, R., Lefebvre, B. A., William, A. J., Rutkowske, R. D., Spitzer, T. D., 2007. Automated structure verification based on a combination of 1D 1H NMR and 2D 1H-13C HSQC spectra. Magn. Reson. Chem. 45(10), 803-813.
Hyami, K-I., Funatsu, K., Sasaki, S-I., 1993. Improvement in the structure generation of the automated structure elucidation system for organic compounds, CHEMICS. Bunseki Kagaku 42(6), 369-374.
Jayaseelan, K. V., Steinbeck, C., 2014. Building blocks for automated elucidation of metabolites: natural product-likeness for candidate ranking. BMC Bioinformatics 15, 234. 10.1186/1471-2105-15-234.
Kuhn, S., Egert, B., Neumann, S, Steinbeck, C., 2008. Building blocks for automated elucidation of metabolites: Machine-learning methods for NMR prediction. BMC Bioinformatics 9, 400.
Meiler, J., Meusinger, R., Will, M., 1999. Neural network prediction of 13C NMR chemical shifts of substituted benzenes. Monatsh. Chem./Chem. Monthly 130, 1089-1095.
Meiler, J., Meusinger, R., Will, M., 2000. Fast determination of 13C NMR chemical shifts using artificial neural networks. J. Chem. Inf. Comput. Sci. 40, 1169-1176.
Meiler, J., Will, M., 2001. Automated structure elucidation of organic molecules from 13C NMR spectra using genetic algorithms and neural networks. J. Chem. Inf. Comput. Sci. 41, 1535-1546.
Meiler, J., Kock, M., 2004. Novel methods of automated structure elucidation based on 13C NMR spectroscopy. Magn. Reson. Chem. 42(12), 1042-1045.
Munk, E. M., 1998. Computer-based structure determination: Then and now. J. Chem. Inf. Comput. Sci. 38, 997-1009.
Penchev, P. N., Schulz, K-P., Munk, M. E., 2012. INFERCNMR: A 13C NMR interpretive library search system. J. Chem. Inf. Model. 52, 1513-1528.
Peng, C., Yuan, S. G., Zheng, C. Z., Hui, Y. Z., Wu, H. M., Ma, K., Han, X. W., 1994. Application of expert system CISOC-SES to the structure elucidation of complex natural products. J. Chem. Inf. Comput. Sci. 34, 814-819.
Pretsch, E., Furst, A., Robien, W., 1991. Parameter set for the prediction of the 13C NMR chemical shifts of sp2- and sp-hybridized carbon atoms in organic compounds. Anal. Chim. Acta 248, 415-428.
Steinbeck, C., 2004. Recent advances in automated structure elucidation of natural products. Nat. Prod. Rep. 21(4) 512-518.
Svozil, D., Pospichal, J., Kvasnicka, V., 1995. Neural network prediction of carbon-13 NMR chemical shifts of alkanes. J. Chem. Inf. Comput. Sci. 35, 924-928.