Generally, 2D-based compound similarity kernels, such as SIMCOMP ( ), are preferred to predict drug-target prediction. Here are some examples of 2D-based compound similarity techniques to indicate their success. One of the 2D-based compound similarities is TargetHunter, a web-based tool ( ). TargetHunter was trained on ChEMBL data, and PubChem bioassay was utilised as test data ( ). Compared to 2D and 1D representation, SMILES-based similarity may be computationally more efficient than 2Dbased approaches ( ). Consequently, the ligand-based drug discovery approach can be more successful with other techniques, such as De Novo Drug Discovery.