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Accurate prediction of drug-target interaction (DTI) is essential for insilico drug design. For the purpose, we propose a novel approach for predictingDTI using a GNN that directly incorporates the 3D structure of a protein-ligandcomplex. We also apply a distance-aware graph attention algorithm with gateaugmentation to increase the performance of our model. As a result, our modelshows better performance than docking and other deep learning methods for bothvirtual screening and pose prediction. In addition, our model can reproduce thenatural population distribution of active molecules and inactive molecules.

Predicting drug-target interaction using 3D structure-embedded graph representations from graph neural networks