DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lim, Jaechang | ko |
dc.contributor.author | Ryu, Seongok | ko |
dc.contributor.author | Park, Kyubyong | ko |
dc.contributor.author | Choe, Yo Joong | ko |
dc.contributor.author | Ham, Jiyeon | ko |
dc.contributor.author | Kim, Woo Youn | ko |
dc.date.accessioned | 2019-10-15T05:20:04Z | - |
dc.date.available | 2019-10-15T05:20:04Z | - |
dc.date.created | 2019-10-14 | - |
dc.date.created | 2019-10-14 | - |
dc.date.issued | 2019-09 | - |
dc.identifier.citation | JOURNAL OF CHEMICAL INFORMATION AND MODELING, v.59, no.9, pp.3981 - 3988 | - |
dc.identifier.issn | 1549-9596 | - |
dc.identifier.uri | http://hdl.handle.net/10203/267988 | - |
dc.description.abstract | 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. | - |
dc.language | English | - |
dc.publisher | AMER CHEMICAL SOC | - |
dc.title | Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation | - |
dc.type | Article | - |
dc.identifier.wosid | 000487769800041 | - |
dc.identifier.scopusid | 2-s2.0-85072573996 | - |
dc.type.rims | ART | - |
dc.citation.volume | 59 | - |
dc.citation.issue | 9 | - |
dc.citation.beginningpage | 3981 | - |
dc.citation.endingpage | 3988 | - |
dc.citation.publicationname | JOURNAL OF CHEMICAL INFORMATION AND MODELING | - |
dc.identifier.doi | 10.1021/acs.jcim.9b00387 | - |
dc.contributor.localauthor | Kim, Woo Youn | - |
dc.contributor.nonIdAuthor | Park, Kyubyong | - |
dc.contributor.nonIdAuthor | Choe, Yo Joong | - |
dc.contributor.nonIdAuthor | Ham, Jiyeon | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordPlus | BINDING-AFFINITY PREDICTION | - |
dc.subject.keywordPlus | DOCKING | - |
dc.subject.keywordPlus | VALIDATION | - |
dc.subject.keywordPlus | LIGANDS | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.