Predicting drug-target interaction using 3D structure-embedded graph representations from graph neural networks
This work addresses drug design for pharmaceutical research, with incremental improvements in accuracy.
The paper tackled drug-target interaction prediction by proposing a graph neural network that incorporates 3D structure of protein-ligand complexes, achieving better performance than docking and other deep learning methods in virtual screening and pose prediction.
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.