Predicting drug-target interaction using 3D structure-embedded graph representations from graph neural networks
Journal Of Chemical Information and Modeling
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.
임재창 (KAIST), 류성옥 (KAIST), 박규병 (카카오브레인), 최요중 (카카오), 함지연 (카카오브레인), 김우연 (KAIST)