NMR Prediction in Mnova follows the concept of “unity creates strength”. The basic idea is to combine several predictors together to get a better predictive power. We have borrowed from the field of Machine Learning the term "ensemble" to define this new prediction procedure and I have written about it in this article, “Ensemble NMR Prediction” , where some results using 13C NMR data are given.
To complement that article, in this post I will show some results for 1H NMR data. I have taken from the literature a few assigned molecules. Those molecules were not contained in the internal databases of Mnova predictors. The overall results are shown in table 1.
Table 1: Mean
absolute errors for the individual and ensemble predictor. ML stands for Machine Learning
As in the 13C analysis, the new Ensemble Prediction provides a smaller MAE than the individual predictors.
The error distribution also shows that the ensemble method helps reduce the number of prediction outliers
Table 2: Distribution of prediction errors for the
different individual predictors as well as for the final, ensemble result. Freq.
values are in %.
