Target-aware Guided equivariant Diffusion model for 3D molecule Generation

In the process of targeted drug molecule design, models that incorporate three-dimensional structures show better performance than target-free models. This is because the interactions between atoms can be explicitly modeled in three-dimensional space, thereby improving the accuracy and effectiveness of drug design. In previous studies, researchers usually used static or semi-flexible crystal structures to simplify the dynamic interactions between active sites and small molecules, only calculating limited system dynamics information. However, this simplification leads to an inadequate understanding of the dynamic characteristics of active sites, neglecting the consideration of the binding dynamics during molecule generation, which impacts the generation of high-quality 3D molecules. To this end, we proposed a 3D molecule generation method based on a target-aware guided diffusion model. This method uses target structure as a condition, introducing a diffusion model to simulate the dynamic evolution of molecular structures, and further refines and optimizes ligand through the structure of target-ligand complexes. Additionally, the continuous distribution of atomic coordinates and the discrete distribution of molecular features are introduced in the molecular latent space through the equivariant graph neural network, which facilitates the representation of the parameterized reverse generation process. Experimental results show that this method can continuously achieve better performance on multiple molecular generation benchmarks and can generate more realistic multiple 3D structure molecules with high binding affinity to protein targets.