  In silico prediction of the ligand binding pose to a given protein target is
a crucial but challenging task in drug discovery. This work focuses on blind
flexible selfdocking, where we aim to predict the positions, orientations and
conformations of docked molecules. Traditional physics-based methods usually
suffer from inaccurate scoring functions and high inference cost. Recently,
data-driven methods based on deep learning techniques are attracting growing
interest thanks to their efficiency during inference and promising performance.
These methods usually either adopt a two-stage approach by first predicting the
distances between proteins and ligands and then generating the final
coordinates based on the predicted distances, or directly predicting the global
roto-translation of ligands. In this paper, we take a different route. Inspired
by the resounding success of AlphaFold2 for protein structure prediction, we
propose E3Bind, an end-to-end equivariant network that iteratively updates the
ligand pose. E3Bind models the protein-ligand interaction through careful
consideration of the geometric constraints in docking and the local context of
the binding site. Experiments on standard benchmark datasets demonstrate the
superior performance of our end-to-end trainable model compared to traditional
and recently-proposed deep learning methods.
