Structure-based drug design (SBDD) aims to generate 3D ligand molecules that
bind to specific protein targets. Existing 3D deep generative models including
diffusion models have shown great promise for SBDD. However, it is complex to
capture the essential protein-ligand interactions exactly in 3D space for
molecular generation. To address this problem, we propose a novel framework,
namely Binding-Adaptive Diffusion Models (BindDM). In BindDM, we adaptively
extract subcomplex, the essential part of binding sites responsible for
protein-ligand interactions. Then the selected protein-ligand subcomplex is
processed with SE(3)-equivariant neural networks, and transmitted back to each
atom of the complex for augmenting the target-aware 3D molecule diffusion
generation with binding interaction information. We iterate this hierarchical
complex-subcomplex process with cross-hierarchy interaction node for adequately
fusing global binding context between the complex and its corresponding
subcomplex. Empirical studies on the CrossDocked2020 dataset show BindDM can
generate molecules with more realistic 3D structures and higher binding
affinities towards the protein targets, with up to -5.92 Avg. Vina Score, while
maintaining proper molecular properties. Our code is available at
this https URL

基于结构的药物设计旨在生成与特定蛋白质靶点结合的三维配体分子。本研究提出了一种名为 Binding-Adaptive Diffusion Models (BindDM) 的新框架，通过自适应地提取负责蛋白质 - 配体相互作用的结合位点的关键部分，使用 SE (3)- 等变神经网络对所选的蛋白质 - 配体子复合物进行处理，并将其与复合物的每个原子传递回来，以在生成具有结合相互作用信息的面向目标的三维分子扩散时增强。实验证明 BindDM 能够生成具有更真实的三维结构和更高结合亲和力的分子，平均 Vina 得分高达 - 5.92，同时保持良好的分子属性。