“Ask Ernö”: a self-learning tool for assignment and prediction of nuclear magnetic resonance spectra
Tóm tắt
We present “Ask Ernö”, a self-learning system for the automatic analysis of NMR spectra, consisting of integrated chemical shift assignment and prediction tools. The output of the automatic assignment component initializes and improves a database of assigned protons that is used by the chemical shift predictor. In turn, the predictions provided by the latter facilitate improvement of the assignment process. Iteration on these steps allows Ask Ernö to improve its ability to assign and predict spectra without any prior knowledge or assistance from human experts. This concept was tested by training such a system with a dataset of 2341 molecules and their 1H-NMR spectra, and evaluating the accuracy of chemical shift predictions on a test set of 298 partially assigned molecules (2007 assigned protons). After 10 iterations, Ask Ernö was able to decrease its prediction error by 17 %, reaching an average error of 0.265 ppm. Over 60 % of the test chemical shifts were predicted within 0.2 ppm, while only 5 % still presented a prediction error of more than 1 ppm.
Ask Ernö introduces an innovative approach to automatic NMR analysis that constantly learns and improves when provided with new data. Furthermore, it completely avoids the need for manually assigned spectra. This system has the potential to be turned into a fully autonomous tool able to compete with the best alternatives currently available.
Tài liệu tham khảo
ACD/HNMR Predictor v.9.0, Advanced Chemistry Development, Inc., 110 Yonge Street, 14th floor, Toronto, Ontario, Canada M5C1T4
Binev Y, Marques MM, Aires-de-Sousa J (2007) Prediction of 1H NMR coupling constants with associative neural networks trained for chemical shifts. J Chem Inf Model 47(6):2089–2097
Aires-de-Sousa J, Hemmer M, Gasteiger J (2002) Prediction of 1H NMR chemical shifts using neural networks. Anal Chem 74(1):80–90
Binev Y, Corvo M, Aires-de-Sousa J (2004) The impact of available experimental data on the prediction of 1H NMR chemical shifts by neural networks. J Chem Inf Comput Sci 44(3):946–949
Binev Y, Aires-de-Sousa J (2004) Structure-based predictions of 1H NMR chemical shifts using feed-forward neural networks. J Chem Inf Comput Sci 44(3):940–945
Binev Y, Marques MM, Aires-de-Sousa J (2007) Prediction of 1H NMR coupling constants with associative neural networks trained for chemical shifts. J Chem Inf Model 47(6):2089–2097
SPINUS WEB http://www2.ccc.uni-erlangen.de/services/spinus/. Accessed Feb 2016
Modgraph NMR Prediction http://www.modgraph.co.uk/product_nmr.htm. Accessed Feb 2016
ChemDraw 15.0 http://www.cambridgesoft.com/Ensemble_for_Chemistry/ChemDraw/. Accessed Feb 2016
Elyashberg ME, Williams AJ, Martin GE (2008) Computer-assisted structure verification and elucidation tools in NMR-based structure elucidation. Prog Nucl Magn Reson Spectrosc 53:1–104
Plainchont B, Emerenciano VP, Nuzillard J-M (2013) Recent advances in the structure elucidation of small organic molecules by the LSD software. Magn Reson Chem 51:447–453
Griffiths L, Beeley HH, Horton R (2008) Towards the automatic analysis of NMR spectra: part 7. Assignment of 1H by employing both 1H and 1H/13C correlation spectra. Magn Reson Chem 46:818–827
Golotvin SS, Vodopianov E, Pol R, Lefebvre BA, Williams AJ, Rutkowse RD, Spitzer TD (2007) Automated structure verification based on a combination of 1D 1H-NMR and 2D 1H-13C HSQC spectra. Magn Reson Chem 45:803–813
Cobas C, Seoane F, Vaz E, Bernstein MA, Dominguez S, Pérez M, Sýkora S (2013) Automatic assignment of 1H-NMR spectra of small molecules. Magn Reson Chem 51:649–654
Masui H, Hong H (2006) Spec2D: a structure elucidation system based on 1H-NMR and H-H COSY spectra in organic chemistry. J Chem Inf Model 46:775–787
Molodtsov S, Elyashberg ME, Blinov KA, Williams AJ, Martirosian EE, Martin GE, Lefebvre B (2004) Structure elucidation from 2D NMR spectra using the StructEluc expert system: detection and removal of contradictions in the data. J Chem Inf Comput Sci 44:1737–1751
Steinbeck C, Kuhn S (2004) NMRShiftDB - compound identification and structure elucidation support through a free community-built web database. Phytochemistry 65:2711–2717
Meiler J, Will M (2001) Automated structure elucidation of organic molecules from 13C-NMR spectra using genetic algorithms and neural networks. J Chem Inf Model 41:1535–1546
Tarantola A (2005) Inverse problem theory and methods for model parameter estimation. SIAM, Philadelphia
Castillo AM, Bernal A, Patiny L, Wist J (2015) Fully automatic assignment of small molecules’ NMR spectra without relying on chemical shift predictions. Magn Reson Chem 53:603–611. doi:10.1002/mrc.4272
Pretsch E, Bühlmann P, Affolter C (2000) Structure determination of organic compounds. Springer, Berlin
Bremser W (1978) HOSE—a novel substructure code. Anal Chim Acta 103:355–365
Ask Ernö in Github https://github.com/cheminfo/autolearning. Accessed Feb 2016
Kalchhauser H, Robien W (1985) CSEARCH: a computer program for identification of organic compounds and fully automated assignment of carbon-13 nuclear magnetic resonance spectra. J Chem Inf Comput Sci 25:103–108. doi:10.1021/ci00046a010
Modgraph C13 NMR prediction http://www.modgraph.co.uk/product_nmr_HOSE.htm Accessed Feb 2016