Designing molecules to be active and selective for targets has always been at the core of drug discovery. While recent advances in generative AI are revolutionizing drug discovery by efficiently representing desired molecule properties in computationally amenable latent space, the 3D binding structure would be the most critical but challenging factor to incorporate with, among others, those tailoring molecule structures and thus property. In our talk, we present a 3D-structure-based generative AI method capable of simultaneously generating active small molecules and their putative binding modes for a given target. Notably, incorporating a 3D network strongly enhanced the structural compatibility of the generated molecules in the target binding pocket as well as their synthetic feasibility compared to those of the ligand-based 2D generative modeling. Furthermore, a massive docking simulation for the generated molecules recapitulated the co-generated binding mode. In addition, a significant correlation was found between docking pose ranks and contact recovery rates, implying that the model could learn the underlying physics, albeit not explicitly trained. Furthermore, we also present the extensibility of our 3D generative approach to generating molecules with specific activity profiles against multiple protein receptors. Overall, our study demonstrates the importance of explicitly including 3D protein information in the molecule generation process for AI-driven drug discovery, rather than just including the 3D information to filter generated molecules, providing insight into generative modeling for multiple on/off-targets.