Generative models offer a distinct approach by learning to represent and optimize molecules in a continuous latent space . They have proven to be highly effective in generating onedimensional (1D) molecules with SMILES (Simplified molecular-input line-entry system) representations and two-dimensional (2D) molecules with molecular graphs representations . This advancement holds the potential to accelerate the hit discovery process and minimize the requirement for evaluating hundreds of thousands of candidate compounds . Despite their advancements, 2D generative models have certain limitations in representing molecular structures and generating chemical diversity. These models are based on linear sequences of atoms and bonds, which restricts their ability to capture structural features like stereochemistry and conformational flexibility. Moreover, they often rely on predefined templates, resulting in the generation of similar molecules with limited novelty. Additionally, 2D generative models may not adequately capture the complexity of the target pocket. These limitations underscore the necessity for more advanced modeling approaches, such as three-dimensional (3D) generative models, to address these challenges in drug discovery.