Fig. 2: Model prediction and alignment accuracy. | Communications Chemistry

Fig. 2: Model prediction and alignment accuracy.

From: TransPeakNet for solvent-aware 2D NMR prediction via multi-task pre-training and unsupervised learning

Fig. 2

A MAEs of C--H shift prediction on test dataset. B Peak assignment accuracy by comparing algorithm-generated annotations with expert annotations. Out of the 479 molecules in the test set, 456 molecules have all peaks annotated correctly. For the remaining 23 molecules, 81.56% of the peaks agree with expert annotations. C An example of using our model to accurately predict cross peaks and align them with experimental signals. The molecule is shown at the top-left, where each C--H bond is labeled with a numerical identifier. Notably, the symmetric pairs of bonds (labeled as “2”, “3”, and “4”) are each expected to generate a single HSQC cross peak due to their structural equivalence. The HSQC cross-peaks predicted by our model (in orange) and their alignments to the experimental observations (in blue) are plotted in the right. The alignments are indicated by the dash circles.

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