DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based
  Drug Design
Jiaqi Guan Xiangxin Zhou Yuwei Yang Yu Bao Jian Peng Jianzhu Ma Qiang Liu Liang Wang Quanquan Gu
Designing 3D ligands within a target binding site is a fundamental task in
drug discovery. Existing structured-based drug design methods treat all ligand
atoms equally, which ignores different roles of atoms in the ligand for drug
design and can be less efficient for exploring the large drug-like molecule
space. In this paper, inspired by the convention in pharmaceutical practice, we
decompose the ligand molecule into two parts, namely arms and scaffold, and
propose a new diffusion model, DecompDiff, with decomposed priors over arms and
scaffold. In order to facilitate the decomposed generation and improve the
properties of the generated molecules, we incorporate both bond diffusion in
the model and additional validity guidance in the sampling phase. Extensive
experiments on CrossDocked2020 show that our approach achieves state-of-the-art
performance in generating high-affinity molecules while maintaining proper
molecular properties and conformational stability, with up to -8.39 Avg. Vina
Dock score and 24.5 Success Rate. The code is provided at
https://github.com/bytedance/DecompDiff