Designing high-affinity 3D drug molecules via geometric spatial perception diffusion model

Designing high-affinity molecules for certain proteins is a fundamental and challenging problem for drug discovery, particularly when considering atomic interactions between molecules and proteins in 3D space. Current 3D molecular design methods are limited because they do not adequately capture the ligand molecular position information in Euclidean space. We proposed a diffusion model based on SE(3)-equivariant graph neural networks to enhance generated molecular binding affinity to protein targets using the long-range and distance-aware attention head mix. We also presented a molecular geometry feature enhancement strategy, further strengthening the perception of the spatial size of ligand molecules. Results show that, on the CrossDocked2020 dataset, our model outperforms the existing state-of-the-art models across various affinity-related metrics, including the Vina Score, and preserves essential drug-like properties. Our model excels in designing ligand molecules with macrocyclic structures. Additionally, it offers a moderate level of interpretability, aiding in understanding the binding interactions between 3D drug molecules and protein pockets.