  Predicting the binding structure of a small molecule ligand to a protein -- a
task known as molecular docking -- is critical to drug design. Recent deep
learning methods that treat docking as a regression problem have decreased
runtime compared to traditional search-based methods but have yet to offer
substantial improvements in accuracy. We instead frame molecular docking as a
generative modeling problem and develop DiffDock, a diffusion generative model
over the non-Euclidean manifold of ligand poses. To do so, we map this manifold
to the product space of the degrees of freedom (translational, rotational, and
torsional) involved in docking and develop an efficient diffusion process on
this space. Empirically, DiffDock obtains a 38% 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. Moreover, while
previous methods are not able to dock on computationally folded structures
(maximum accuracy 10.4%), DiffDock maintains significantly higher precision
(21.7%). Finally, DiffDock has fast inference times and provides confidence
estimates with high selective accuracy.
