Journal
JOURNAL OF CHEMICAL INFORMATION AND MODELING
Volume 59, Issue 9, Pages 3981-3988Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jcim.9b00387
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Funding
- National Research Foundation of Korea (NRF) - Korea government (MSIT) [NRF-2017R1E1A1A01078109]
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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.
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