TY - JOUR
AU - Kim, Woo Youn
AB - 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.
TI - Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation.
JF - Journal of Chemical Information and Modeling
DO - 10.1021/acs.jcim.9b00387
DA - 2020-09-15
UR - https://www.deepdyve.com/lp/pubmed/predicting-drug-target-interaction-using-a-novel-graph-neural-network-TDEY6Qcsuy
SP - 3981
EP - 3988
VL - 59
IS - 9
DP - DeepDyve
ER -