Accurate prediction of protein-ligand binding structures, a task known as
molecular docking is crucial for drug design but remains challenging. While
deep learning has shown promise, existing methods often depend on holo-protein
structures (docked, and not accessible in realistic tasks) or neglect pocket
sidechain conformations, leading to limited practical utility and unrealistic
conformation predictions. To fill these gaps, we introduce an under-explored
task, named flexible docking to predict poses of ligand and pocket sidechains
simultaneously and introduce Re-Dock, a novel diffusion bridge generative model
extended to geometric manifolds. Specifically, we propose energy-to-geometry
mapping inspired by the Newton-Euler equation to co-model the binding energy
and conformations for reflecting the energy-constrained docking generative
process. Comprehensive experiments on designed benchmark datasets including
apo-dock and cross-dock demonstrate our model's superior effectiveness and
efficiency over current methods.

通过引入新的任务 —— 灵活对接，以预测配体和口袋侧链的姿态，并引入扩展到几何流形的扩散桥生成模型 Re-Dock，提出了受牛顿 - 欧拉方程启发的能量到几何映射，用于共同建模反映能量约束对接生成过程的结合能和构型，在设计的基准数据集如 apo-dock 和 cross-dock 上进行综合实验证明我们模型相对于现有方法具有更高的有效性和效率。