  Structure-based drug design (SBDD) aims to design small-molecule ligands that
bind with high affinity and specificity to pre-determined protein targets. In
this paper, we formulate SBDD as a 3D-conditional generation problem and
present DiffSBDD, an SE(3)-equivariant 3D-conditional diffusion model that
generates novel ligands conditioned on protein pockets. Comprehensive in silico
experiments demonstrate the efficiency and effectiveness of DiffSBDD in
generating novel and diverse drug-like ligands with competitive docking scores.
We further explore the flexibility of the diffusion framework for a broader
range of tasks in drug design campaigns, such as off-the-shelf property
optimization and partial molecular design with inpainting.
