One of the unique models that addressed the synthesizability of generated molecules is DeepLigBuilder+ , its framework addresses both 3D structure information and reaction-based pathways, supporting the retro-synthetic analysis. The model is a combination of a reinforcement learning method based on Monte Carlo tree search (MCTS), as a synthesizability constraint depending on purchasable building blocks, and an SE (3)-equivariant transformer conditioned on the shape and pharmacophore-based inputs. Also, a masking strategy was applied on each step; to guarantee the generation of synthesizable synthons. Two case studies were evaluated; inhibitor compounds targeting ATP-binding pocket of Bruton's tyrosine kinase (BTK) and the NAD+binding pocket of human phosphoglycerate dehydrogenase (PHGDH), they have shown high predicted binding affinity and suitable binding modes within a proper synthesizability constraint. Furthermore, to reach the structure-based generation capability, they created a dataset of pharmacophore-ligand pairs using large-scale 3D alignment of molecules, and then use it to develop a novel SE (3)-equivariant transformer conditioned on 3D information, followed by MCTS as rolling out policy; resulting in a significantly increased search speed.