Deep generative modeling has a strong potential to accelerate drug design. However,
existing generative models often face challenges in generalization due to limited data,
leading to less innovative designs with often unfavorable interactions for unseen target
proteins. To address these issues, we propose an interaction-aware 3D molecular
generative framework that enables interaction-guided drug design inside target binding
pockets. By leveraging universal patterns of protein-ligand interactions as prior knowledge …