Abstract

BackgroundWe 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.ResultsThis 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.ConclusionsAsk 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.Graphical abstractSelf-learning loop. Any progress in the prediction (forward problem) will improve the assignment ability (reverse problem) and vice versa.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-016-0134-6) contains supplementary material, which is available to authorized users.

Highlights

  • We present “Ask Ernö”, a self-learning system for the automatic analysis of nuclear magnetic resonance (NMR) spectra, consisting of integrated chemical shift assignment and prediction tools

  • This reflects in existing computational tools for NMR elucidation and assignment: either they are not fullyautomatic, requiring preliminary analysis by the user [10, 11], or resort to chemical shift predictions [10, 12,13,14,15,16,17,18] that rely on databases of spectra assigned ‘manually’ by trained experts

  • To get a more detailed picture of Ask Ernö’s performance and learning process we looked at three indicators: prediction error, prediction uncertainty, and the fraction of chemical shifts from the test set that could be predicted

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Summary

Introduction

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 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. The two problems are strongly related, a fact that poses an important limitation to the automation of NMR analysis This reflects in existing computational tools for NMR elucidation and assignment: either they are not fullyautomatic, requiring preliminary analysis by the user [10, 11], or resort to chemical shift predictions [10, 12,13,14,15,16,17,18] that rely on databases of spectra assigned ‘manually’ by trained experts. Regardless of the approach, a significant amount of labour is involved that is certainly not devoid of human errors

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