  Docking is an important tool in computational drug discovery that aims to
predict the binding pose of a ligand to a target protein through a combination
of pose scoring and optimization. A scoring function that is differentiable
with respect to atom positions can be used for both scoring and gradient-based
optimization of poses for docking. Using a differentiable grid-based atomic
representation as input, we demonstrate that a scoring function learned by
training a convolutional neural network (CNN) to identify binding poses can
also be applied to pose optimization. We also show that an iteratively-trained
CNN that includes poses optimized by the first CNN in its training set performs
even better at optimizing randomly initialized poses than either the first CNN
scoring function or AutoDock Vina.
