{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T17:52:34Z","timestamp":1780336354285,"version":"3.54.1"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2022,10,23]],"date-time":"2022-10-23T00:00:00Z","timestamp":1666483200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Hong Kong Special Administrative Region","award":["11200218"],"award-info":[{"award-number":["11200218"]}]},{"DOI":"10.13039\/501100005847","name":"Health and Medical Research Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005847","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hong Kong Special Administrative Region","award":["07181426"],"award-info":[{"award-number":["07181426"]}]},{"DOI":"10.13039\/100017449","name":"Hong Kong Institute for Data Science","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100017449","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007567","name":"City University of Hong Kong","doi-asserted-by":"publisher","award":["CityU 11202219"],"award-info":[{"award-number":["CityU 11202219"]}],"id":[{"id":"10.13039\/100007567","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007567","name":"City University of Hong Kong","doi-asserted-by":"publisher","award":["CityU 11203520"],"award-info":[{"award-number":["CityU 11203520"]}],"id":[{"id":"10.13039\/100007567","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32000464"],"award-info":[{"award-number":["32000464"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016588","name":"Shenzhen Research Institute, City University of Hong Kong","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100016588","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,11,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The identification of drug\u2013target interactions (DTIs) plays a vital role for in silico drug discovery, in which the drug is the chemical molecule, and the target is the protein residues in the binding pocket. Manual DTI annotation approaches remain reliable; however, it is notoriously laborious and time-consuming to test each drug\u2013target pair exhaustively. Recently, the rapid growth of labelled DTI data has catalysed interests in high-throughput DTI prediction. Unfortunately, those methods highly rely on the manual features denoted by human, leading to errors.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Here, we developed an end-to-end deep learning framework called CoaDTI to significantly improve the efficiency and interpretability of drug target annotation. CoaDTI incorporates the Co-attention mechanism to model the interaction information from the drug modality and protein modality. In particular, CoaDTI incorporates transformer to learn the protein representations from raw amino acid sequences, and GraphSage to extract the molecule graph features from SMILES. Furthermore, we proposed to employ the transfer learning strategy to encode protein features by pre-trained transformer to address the issue of scarce labelled data. The experimental results demonstrate that CoaDTI achieves competitive performance on three public datasets compared with state-of-the-art models. In addition, the transfer learning strategy further boosts the performance to an unprecedented level. The extended study reveals that CoaDTI can identify novel DTIs such as reactions between candidate drugs and severe acute respiratory syndrome coronavirus 2-associated proteins. The visualization of co-attention scores can illustrate the interpretability of our model for mechanistic insights.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability<\/jats:title>\n                  <jats:p>Source code are publicly available at https:\/\/github.com\/Layne-Huang\/CoaDTI.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bib\/bbac446","type":"journal-article","created":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T05:58:03Z","timestamp":1666591083000},"source":"Crossref","is-referenced-by-count":86,"title":["CoaDTI: multi-modal co-attention based framework for drug\u2013target interaction annotation"],"prefix":"10.1093","volume":"23","author":[{"given":"Lei","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong , Hong Kong SAR"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiecong","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Pathology, Harvard Medical School , Boston , USA"},{"name":"Department of Computer Science, The University of Hong Kong , Hong Kong SAR"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong , Hong Kong SAR"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zetian","family":"Zheng","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong , Hong Kong SAR"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingkuan","family":"Meng","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong , Hong Kong SAR"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingjian","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong , Hong Kong SAR"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangtao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Jilin University , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ka-Chun","family":"Wong","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong , Hong Kong SAR"},{"name":"Hong Kong Institute for Data Science, City University of Hong Kong , Hong Kong SAR"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,10,23]]},"reference":[{"issue":"18","key":"2022112111200683200_ref1","doi-asserted-by":"crossref","first-page":"i522","DOI":"10.1093\/bioinformatics\/bts383","article-title":"Relating drug\u2013protein interaction network with drug side effects","volume":"28","author":"Mizutani","year":"2012","journal-title":"Bioinformatics"},{"issue":"5","key":"2022112111200683200_ref2","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1038\/nrd1720","article-title":"Elucidating mechanisms of drug-induced toxicity","volume":"4","author":"Liebler","year":"2005","journal-title":"Nat Rev Drug Discov"},{"issue":"2","key":"2022112111200683200_ref3","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1093\/bioinformatics\/bty535","article-title":"Compound\u2013protein interaction prediction with end-to-end learning of neural networks for graphs and sequences","volume":"35","author":"Tsubaki","year":"2019","journal-title":"Bioinformatics"},{"issue":"6","key":"2022112111200683200_ref4","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1111\/j.1476-5381.2010.01127.x","article-title":"Principles of early drug discovery","volume":"162","author":"Hughes","year":"2011","journal-title":"Br J Pharmacol"},{"issue":"6604 Suppl","key":"2022112111200683200_ref5","first-page":"14","article-title":"High-throughput screening for drug discovery","volume":"384","author":"Broach","year":"1996","journal-title":"Nature"},{"issue":"2","key":"2022112111200683200_ref6","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1038\/s42256-020-0152-y","article-title":"Predicting drug\u2013protein interaction using quasi-visual question answering system","volume":"2","author":"Zheng","year":"2020","journal-title":"Nature Machine Intelligence"},{"issue":"5","key":"2022112111200683200_ref7","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1093\/bib\/bbt056","article-title":"Similarity-based machine learning methods for predicting drug\u2013target interactions: a brief review","volume":"15","author":"Ding","year":"2014","journal-title":"Brief Bioinform"},{"issue":"3","key":"2022112111200683200_ref8","doi-asserted-by":"crossref","first-page":"1006","DOI":"10.1039\/C5MB00650C","article-title":"An improved approach for predicting drug\u2013target interaction: proteochemometrics to molecular docking","volume":"12","author":"Shaikh","year":"2016","journal-title":"Mol Biosyst"},{"key":"2022112111200683200_ref9","first-page":"3428","article-title":"Bayesian neural network with pretrained protein embedding enhances prediction accuracy of drug-protein interaction","volume-title":"Bioinformatics","author":"Kim","year":"2020"},{"issue":"4","key":"2022112111200683200_ref10","doi-asserted-by":"crossref","first-page":"942","DOI":"10.1021\/acs.jcim.6b00740","article-title":"Protein\u2013ligand scoring with convolutional neural networks","volume":"57","author":"Ragoza","year":"2017","journal-title":"J Chem Inf Model"},{"issue":"2","key":"2022112111200683200_ref11","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1093\/bioinformatics\/btm580","article-title":"Genome scale enzyme\u2013metabolite and drug\u2013target interaction predictions using the signature molecular descriptor","volume":"24","author":"Faulon","year":"2008","journal-title":"Bioinformatics"},{"key":"2022112111200683200_ref12","first-page":"1236","volume-title":"2016 international joint conference on neural networks (IJCNN)","author":"Peng-Wei","year":"2016"},{"key":"2022112111200683200_ref13","first-page":"3371","volume-title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18","author":"Gao","year":"2018"},{"key":"2022112111200683200_ref14","doi-asserted-by":"crossref","DOI":"10.3115\/v1\/W14-4012","article-title":"On the properties of neural machine translation: Encoder-decoder approaches","volume-title":"Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation","author":"Cho","year":"2014"},{"key":"2022112111200683200_ref15","article-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","volume-title":"NIPS 2014 Workshop on Deep Learning","author":"Chung","year":"2014"},{"key":"2022112111200683200_ref16","volume-title":"Thirteenth annual conference of the international speech communication association","author":"Sundermeyer","year":"2012"},{"key":"2022112111200683200_ref17","first-page":"5998","volume-title":"Advances in neural information processing systems","author":"Vaswani","year":"2017"},{"key":"2022112111200683200_ref18","article-title":"An image is worth 16x16 words: Transformers for image recognition at scale","volume-title":"International Conference on Learning Representations","author":"Dosovitskiy","year":"2020"},{"issue":"23","key":"2022112111200683200_ref19","doi-asserted-by":"crossref","first-page":"4485","DOI":"10.1093\/bioinformatics\/btab473","article-title":"Multidti: drug\u2013target interaction prediction based on multi-modal representation learning to bridge the gap between new chemical entities and known heterogeneous network","volume":"37","author":"Zhou","year":"2021","journal-title":"Bioinformatics"},{"issue":"1","key":"2022112111200683200_ref20","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","article-title":"Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. The graph neural network model","volume":"20","author":"Scarselli","year":"2008","journal-title":"IEEE Trans Neural Netw"},{"issue":"14","key":"2022112111200683200_ref21","doi-asserted-by":"crossref","first-page":"3389","DOI":"10.3390\/ijms20143389","article-title":"Chemi-net: a molecular graph convolutional network for accurate drug property prediction","volume":"20","author":"Liu","year":"2019","journal-title":"Int J Mol Sci"},{"issue":"19","key":"2022112111200683200_ref22","first-page":"83","article-title":"Convolutional neural network based on smiles representation of compounds for detecting chemical motif","volume":"19","author":"Hirohara","year":"2018","journal-title":"BMC bioinformatics"},{"issue":"8","key":"2022112111200683200_ref23","doi-asserted-by":"crossref","first-page":"1140","DOI":"10.1093\/bioinformatics\/btaa921","article-title":"Graphdta: Predicting drug\u2013target binding affinity with graph neural networks","volume":"37","author":"Nguyen","year":"2021","journal-title":"Bioinformatics"},{"key":"2022112111200683200_ref24","first-page":"1025","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","author":"Hamilton","year":"2017"},{"key":"2022112111200683200_ref25","first-page":"6281","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Zhou","year":"2019"},{"key":"2022112111200683200_ref26","first-page":"270","volume-title":"International conference on artificial neural networks","author":"Tan","year":"2018"},{"key":"2022112111200683200_ref27","volume-title":"International Conference on Machine Learning","author":"Costa","year":"2010"},{"key":"2022112111200683200_ref28","first-page":"4171","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)","author":"Devlin","year":"2019"},{"issue":"4","key":"2022112111200683200_ref29","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.2113348119","article-title":"Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterized proteins","volume":"119","author":"Kandathil","year":"2022","journal-title":"Proc Natl Acad Sci"},{"key":"2022112111200683200_ref30","volume-title":"Advances in Neural Information Processing Systems","author":"Zhang","year":"2019"},{"issue":"12","key":"2022112111200683200_ref31","doi-asserted-by":"crossref","first-page":"i221","DOI":"10.1093\/bioinformatics\/btv256","article-title":"Improving compound\u2013protein interaction prediction by building up highly credible negative samples","volume":"31","author":"Liu","year":"2015","journal-title":"Bioinformatics"},{"issue":"D1","key":"2022112111200683200_ref32","doi-asserted-by":"crossref","first-page":"D1045","DOI":"10.1093\/nar\/gkv1072","article-title":"Bindingdb in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology","volume":"44","author":"Gilson","year":"2016","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"2022112111200683200_ref33","first-page":"1","article-title":"Could graph neural networks learn better molecular representation for drug discovery? a comparison study of descriptor-based and graph-based models","volume":"13","author":"Jiang","year":"2021","journal-title":"J Chem"},{"key":"2022112111200683200_ref34","first-page":"774","volume-title":"European Semantic Web Conference","author":"Fokoue","year":"2016"},{"issue":"4","key":"2022112111200683200_ref35","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.1021\/acs.jproteome.6b00618","article-title":"Deep-learning-based drug\u2013target interaction prediction","volume":"16","author":"Wen","year":"2017","journal-title":"J Proteome Res"},{"issue":"4","key":"2022112111200683200_ref36","doi-asserted-by":"crossref","first-page":"930","DOI":"10.1213\/ANE.0000000000005292","article-title":"Aspirin use is associated with decreased mechanical ventilation, intensive care unit admission, and in-hospital mortality in hospitalized patients with coronavirus disease 2019","volume":"132","author":"Chow","year":"2021","journal-title":"Anesthesia & Analgesia"},{"issue":"10279","key":"2022112111200683200_ref37","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1016\/S0140-6736(21)00461-X","article-title":"Azithromycin for community treatment of suspected covid-19 in people at increased risk of an adverse clinical course in the uk (principle): a randomised, controlled, open-label, adaptive platform trial","volume":"397","author":"Butler","year":"2021","journal-title":"The Lancet"},{"issue":"1","key":"2022112111200683200_ref38","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1002\/pro.3943","article-title":"Ucsf chimerax: Structure visualization for researchers, educators, and developers","volume":"30","author":"Pettersen","year":"2021","journal-title":"Protein Sci"},{"issue":"D1","key":"2022112111200683200_ref39","doi-asserted-by":"crossref","first-page":"D1388","DOI":"10.1093\/nar\/gkaa971","article-title":"Pubchem in 2021: new data content and improved web interfaces","volume":"49","author":"Kim","year":"2021","journal-title":"Nucleic Acids Res"},{"issue":"3","key":"2022112111200683200_ref40","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1021\/ci400709d","article-title":"Making sense of large-scale kinase inhibitor bioactivity data sets: a comparative and integrative analysis","volume":"54","author":"Tang","year":"2014","journal-title":"J Chem Inf Model"},{"issue":"11","key":"2022112111200683200_ref41","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.1038\/nbt.1990","article-title":"Comprehensive analysis of kinase inhibitor selectivity","volume":"29","author":"Davis","year":"2011","journal-title":"Nat Biotechnol"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/6\/bbac446\/47144117\/bbac446.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/6\/bbac446\/47144117\/bbac446.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,21]],"date-time":"2022-11-21T11:25:55Z","timestamp":1669029955000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbac446\/6770087"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,23]]},"references-count":41,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2022,11,19]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbac446","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,11]]},"published":{"date-parts":[[2022,10,23]]},"article-number":"bbac446"}}