There have been some attempts to tackle these challenges. For instance, Cotrell et al. introduced SMART, a technique that leverages a neural network trained on HSQC spectra, to enhance natural products deduplication by identifying clusters of similar compounds. DeepSAT developed from the same group employs a neural network-based scaffold prediction system based on chemical features from HSQC spectra. Another 2D method, developed by Sarotti et al., is based on an artificial neural network (ANN-PRA) trained on pattern recognition descriptors of DFT-calculated and experimental HSQC data, which could correctly identify mischaracterized structures from the literature. These approaches, though novel and effective to a certain extent, do bring their own limitations, especially as the performance of a neural network can depend heavily on the diversity of structures included in the training set. As such, they may not fully address the complexities associated with NMR data, such as instrument variability, experimental conditions, and the symmetry or chirality effects of molecules.