A common complication for these algorithms stems from the variance in peak numbers across different spectra. Even for the same molecule, the number of peaks can vary due to experimental setup or spectral simulation technique used. For example, in experimental data, some peaks can disappear among the noise, or the measured spectrum can contain numerous exceptions to rule-based splitting patterns, which are dependent on the specific molecular structure and the resolution of the NMR instrument. In simulated spectra, all depends on the type of method used. Most common ways to simulate HSQC data are (1) commercial software like ACD Labs (ACD) or MestReNova (MNova), (2) quantum chemical calculations with for example Density Functional Theory (DFT), and (3) machine learning (ML) models. ACD and MNova create their 2D spectra using proprietary algorithms, which include an ensemble of techniques such as calculating chemical shifts based on databases of empirical data, implementation of Hierarchical Organization of Spherical Environments (HOSE) and neural network predictions. Finally, the 1D generated outputs of these software solutions reconstruct a simulated 2D spectrum in the form of a set of peak coordinates in the 1 H- C space, representing the molecular fingerprint of the compound. DFT calculations compute proton ( 1 H) and carbon ( 13 C) chemical shifts for a given molecular structure. A ML model such as the one implemented in this research is based on a graph neural network trained on the NMRShiftDB2 database to reproduce experimental 1 H and C chemical shifts of small druglike molecules. While commercial solutions provide algorithms that reconstruct the 2D spectrum from computed C and 1 H NMR chemical shifts, DFT and ML leave this part up to the user. Considering the multitude of sources to obtain HSQC spectra, it is no surprise that the number of spectral peaks will vary. Hence, this necessitates padding strategies to accommodate this mismatch in peak numbers. Evaluating the most efficient strategy becomes paramount, particularly when it comes to tasks like database searching or correctly identifying molecular structures.