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  atoms thus incorporating side-chain flexibility
implicitly.