  Prediction of protein-ligand complexes for flexible proteins remains still a
challenging problem in computational structural biology and drug design. Here
we present two novel deep neural network approaches with significant
improvement in efficiency and accuracy of binding mode prediction on a large
and diverse set of protein systems compared to standard docking. Whereas the
first graph convolutional network is used for re-ranking poses the second
approach aims to generate and rank poses independent of standard docking
approaches. This novel approach relies on the prediction of distance matrices
between ligand atoms and protein C_alpha atoms thus incorporating side-chain
flexibility implicitly.
