Structure-based drug design (SBDD) aims to design small-molecule ligands that
bind with high affinity and specificity to pre-determined protein targets.
Traditional SBDD pipelines start with large-scale docking of compound libraries
from public databases, thus limiting the exploration of chemical space to
existent previously studied regions. Recent machine learning methods approached
this problem using an atom-by-atom generation approach, which is
computationally expensive. In this paper, we formulate SBDD as a 3D-conditional
generation problem and present DiffSBDD, an E(3)-equivariant 3D-conditional
diffusion model that generates novel ligands conditioned on protein pockets.
Furthermore, we curate a new dataset of experimentally determined binding
complex data from Binding MOAD to provide a realistic binding scenario that
complements the synthetic CrossDocked dataset. Comprehensive in silico
experiments demonstrate the efficiency of DiffSBDD in generating novel and
diverse drug-like ligands that engage protein pockets with high binding
energies as predicted by in silico docking.

使用机器学习方法建立了一个新颖的三维生成模型 DiffSBDD，用于设计小分子配体与指定蛋白质靶点高度亲和并具有特异性。 DiffSBDD 通过生成出新型具有高结合能量的药物配体，展现了显著的效率和多样性。