DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking

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. 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. Moreover, while previous methods are not able to dock on computationally folded structures (maximum accuracy 10.4%), D The biological functions of proteins can be modulated by small molecule ligands (such as drugs) binding to them. Thus, a crucial task in computational drug design is molecular docking--predicting the position, orientation, and conformation of a ligand when bound to a target protein--from which the effect of the ligand (if any) might be ...