  Accurate prediction of drug-target interaction (DTI) is essential for in
silico drug design. For the purpose, we propose a novel approach for predicting
DTI using a GNN that directly incorporates the 3D structure of a protein-ligand
complex. We also apply a distance-aware graph attention algorithm with gate
augmentation to increase the performance of our model. As a result, our model
shows better performance than docking and other deep learning methods for both
virtual screening and pose prediction. In addition, our model can reproduce the
natural population distribution of active molecules and inactive molecules.
