Predicting the binding structure of a small molecule to a protein-a task known as molecular docking-is critical to drug design. Recent deep learning methods that frame docking as a regression problem have yet to offer substantial improvements over traditional search-based methods. We identify the drawbacks of a regression-based approach and instead view molecular docking as a generative modeling problem. We develop DockDiff, a novel diffusion process and generative model over the main degrees of freedom involved during docking. Empirically, DockDiff obtains a 37% top-1 success rate (RMSD <2A) on PDBBind, significantly outperforming the previous state-of-the-art of traditional docking (23%) and deep learning (20%) methods.