Binding-Adaptive Diffusion Models for Structure-Based Drug Design
Zhilin Huang Ling Yang Zaixi Zhang Xiangxin Zhou Yu Bao Xiawu Zheng Yuwei Yang Yu Wang Wenming Yang
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
https://github.com/YangLing0818/BindDM