docking is a critical process in structure-based drug discovery to predict the binding conformations between a protein and a small molecule ligand. Recently, deep learning-based methods have achieved promising performance over traditional physics-based search-and-score methods. Despite their success on accurately predicting the binding poses of the small molecule ligands, modeling of protein flexibility and dynamics still remains largely unexplored for docking. We observe that models that do not account for the protein flexibility suffer a large performance drop in cases where proteins undergo large conformational changes upon ligand binding. To address this gap, we developed FlexiDock, a compositional alternating neural diffusion process, which include two diffusion models to explicitly model the conformational flexibilities of proteins and ligands, respectively. The compositional diffusion process is inspired by the induced-fit model in flexible docking. We found the compositional diffusion is able to improve the structural prediction of the proteins upon ligand binding. Our method also offers promising insights into modeling proteins' conformational switches.