Show simple item record

dc.contributor.authorZheng, S
dc.contributor.authorLi, Y
dc.contributor.authorChen, S
dc.contributor.authorXu, J
dc.contributor.authorYang, Y
dc.date.accessioned2021-05-31T01:54:34Z
dc.date.available2021-05-31T01:54:34Z
dc.date.issued2020
dc.identifier.issn2522-5839en_US
dc.identifier.doi10.1038/s42256-020-0152-yen_US
dc.identifier.urihttp://hdl.handle.net/10072/397383
dc.description.abstractIdentifying novel drug–protein interactions is crucial for drug discovery. For this purpose, many machine learning-based methods have been developed based on drug descriptors and one-dimensional protein sequences. However, protein sequences cannot accurately reflect the interactions in three-dimensional space. However, direct input of three-dimensional structure is of low efficiency due to the sparse three-dimensional matrix, and is also prevented by the limited number of co-crystal structures available for training. Here we propose an end-to-end deep learning framework to predict the interactions by representing proteins with a two-dimensional distance map from monomer structures (Image) and drugs with molecular linear notation (String), following the visual question answering mode. For efficient training of the system, we introduce a dynamic attentive convolutional neural network to learn fixed-size representations from the variable-length distance maps and a self-attentional sequential model to automatically extract semantic features from the linear notations. Extensive experiments demonstrate that our model obtains competitive performance against state-of-the-art baselines on the directory of useful decoys, enhanced (DUD-E), human and BindingDB benchmark datasets. Further attention visualization provides biological interpretation to depict highlighted regions of both protein and drug molecules.en_US
dc.description.peerreviewedYesen_US
dc.languageEnglishen_US
dc.language.isoeng
dc.publisherSpringer Science and Business Media LLCen_US
dc.relation.ispartofpagefrom134en_US
dc.relation.ispartofpageto140en_US
dc.relation.ispartofissue2en_US
dc.relation.ispartofjournalNature Machine Intelligenceen_US
dc.relation.ispartofvolume2en_US
dc.titlePredicting drug–protein interaction using quasi-visual question answering systemen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Articlesen_US
dcterms.bibliographicCitationZheng, S; Li, Y; Chen, S; Xu, J; Yang, Y, Predicting drug–protein interaction using quasi-visual question answering system, Nature Machine Intelligence, 2020, 2 (2), pp. 134-140en_US
dc.date.updated2020-09-11T02:59:54Z
dc.description.versionAccepted Manuscript (AM)en_US
gro.rights.copyright© 2020 Nature Publishing Group. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal website for access to the definitive, published version.en_US
gro.hasfulltextFull Text
gro.griffith.authorYang, Yuedong


Files in this item

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record