Predicting drug-target interaction using a novel graph neural network with 3D structure-embedded graph representation

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dc.contributor.author임재창ko
dc.contributor.author류성옥ko
dc.contributor.author박규병ko
dc.contributor.author최요중ko
dc.contributor.author함지연ko
dc.contributor.author김우연ko
dc.date.accessioned2019-12-30T10:20:39Z-
dc.date.available2019-12-30T10:20:39Z-
dc.date.created2019-12-30-
dc.date.issued2019-07-08-
dc.identifier.citation제130차 대한화학회 물리화학분과회 하계 심포지엄-
dc.identifier.urihttp://hdl.handle.net/10203/270785-
dc.description.abstractAccurate prediction of drug-target interaction (DTI) is essential for reliable in-silico drug design. Recently, deep learning techniques with exponentially growing bioactivity data are attracting considerable attention as a promising alternative for predicting DTI. Accordingly, we propose a novel deep learning approach for predicting drug-target interaction using a graph neural network. Graph neural networks show remarkable performance in predicting various molecular properties. Specifically, 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 specific 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). Also, it can reproduce the natural population distribution of active molecules and inactive molecules. Finally, we analyzed the common limitations of our model and other deep learning-based DTI models in generalization ability.-
dc.languageEnglish-
dc.publisher대한화학회 물리화학분과회-
dc.titlePredicting drug-target interaction using a novel graph neural network with 3D structure-embedded graph representation-
dc.typeConference-
dc.type.rimsCONF-
dc.citation.publicationname제130차 대한화학회 물리화학분과회 하계 심포지엄-
dc.identifier.conferencecountryKO-
dc.identifier.conferencelocation부산 해운대 한화리조트-
dc.contributor.localauthor김우연-
dc.contributor.nonIdAuthor임재창-
dc.contributor.nonIdAuthor류성옥-
dc.contributor.nonIdAuthor박규병-
dc.contributor.nonIdAuthor최요중-
dc.contributor.nonIdAuthor함지연-
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CH-Conference Papers(학술회의논문)
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