Molecular docking helps us understand how small-molecules interact with proteins. This is especially useful in early drug development stages such as target identification and compound screening. Quick and accurate docking software allows researchers to focus their attention on a smaller set of lead molecules for further testing. Traditionally, docking software has employed first principles from physics and chemistry. Recently, deep learning has become all the rage for molecular docking, maybe motivated by the successful application of deep learning to molecular folding.

Method

EquiBind is a deep learning unconstrained docking method which models a fixed receptor and a ligand with selected rotatable bonds. It predicts the binding pocket and the ligand’s conformation within the pocket in one go. Under the hood, EquiBind employs two great ideas from a recent ICLR 2022 Paper: a SE3-invariant graph neural network based architecture and the idea to generate fixed sets of matching key points to define a rotation and translation between receptor and ligand. In addition, the authors innovate a fast method to project a deformed ligand onto the space spanned by the rotatable bonds of a pre-generated ligand conformation.