Predicting Drug–Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation

• Published in: Journal of chemical information and modeling Vol. 59; no. 9; pp. 3981 - 3988
• Main Authors: Lim, Jaechang, Ryu, Seongok, Park, Kyubyong, Choe, Yo Joong, Ham, Jiyeon, Kim, Woo Youn
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 23.09.2019
• Subjects: Algorithms; Deep learning; Feature extraction; Graph neural networks; Graph representations; Graphical representations; Ligands; Machine learning; Neural networks; Population distribution; Predictions
• ISSN: 1549-9596; 1549-960X; 1549-960X
• DOI: 10.1021/acs.jcim.9b00387

We propose a novel deep learning approach for predicting drug–target interaction using a graph neural network. We introduce a distance-aware graph attention algorithm to differentiate various types of intermolecular interactions. Furthermore, we extract the graph feature of intermolecular interactions directly from the 3D structural information on the protein–ligand binding pose. Thus, the model can learn key features for accurate predictions of drug–target interaction rather than just memorize certain patterns of ligand molecules. As a result, our model shows better performance than docking and other deep learning methods for both virtual screening (AUROC of 0.968 for the DUD-E test set) and pose prediction (AUROC of 0.935 for the PDBbind test set). In addition, it can reproduce the natural population distribution of active molecules and inactive molecules.