{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T19:32:19Z","timestamp":1785612739467,"version":"3.56.0"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T00:00:00Z","timestamp":1704758400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T00:00:00Z","timestamp":1704758400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1007\/s13042-023-02058-3","type":"journal-article","created":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T06:02:33Z","timestamp":1704780153000},"page":"2709-2718","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Multidta: drug-target binding affinity prediction via representation learning and graph convolutional neural networks"],"prefix":"10.1007","volume":"15","author":[{"given":"Jiejin","family":"Deng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5843-4675","authenticated-orcid":false,"given":"Yijia","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaohua","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaobo","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyu","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,9]]},"reference":[{"issue":"4","key":"2058_CR1","doi-asserted-by":"publisher","first-page":"1337","DOI":"10.1093\/bib\/bby002","volume":"20","author":"A Ezzat","year":"2019","unstructured":"Ezzat A, Wu M, Li X-L, Kwoh C-K (2019) Computational prediction of drug-target interactions using chemogenomic approaches: an empirical survey. Brief Bioinform 20(4):1337\u20131357","journal-title":"Brief Bioinform"},{"issue":"4","key":"2058_CR2","doi-asserted-by":"publisher","first-page":"696","DOI":"10.1093\/bib\/bbv066","volume":"17","author":"X Chen","year":"2016","unstructured":"Chen X, Yan CC, Zhang X, Zhang X, Dai F, Yin J, Zhang Y (2016) Drug-target interaction prediction: databases, web servers and computational models. Brief Bioinform 17(4):696\u2013712","journal-title":"Brief Bioinform"},{"key":"2058_CR3","doi-asserted-by":"crossref","unstructured":"Gao KY, Fokoue A, Luo H, Iyengar A, Ping Z (2018) Interpretable drug target prediction using deep neural representation. In: Twenty-Seventh International Joint Conference on Artificial Intelligence IJCAI-18","DOI":"10.24963\/ijcai.2018\/468"},{"issue":"1","key":"2058_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-016-0977-x","volume":"17","author":"H \u00d6zt\u00fcrk","year":"2016","unstructured":"\u00d6zt\u00fcrk H, Ozkirimli E, \u00d6zg\u00fcr A (2016) A comparative study of smiles-based compound similarity functions for drug-target interaction prediction. BMC Bioinformatics 17(1):1\u201311","journal-title":"BMC Bioinformatics"},{"issue":"3","key":"2058_CR5","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1089\/cmb.2017.0135","volume":"25","author":"L Wang","year":"2018","unstructured":"Wang L, You Z-H, Chen X, Xia S-X, Liu F, Yan X, Zhou Y, Song K-J (2018) A computational-based method for predicting drug-target interactions by using stacked autoencoder deep neural network. J Comput Biol 25(3):361\u2013373","journal-title":"J Comput Biol"},{"issue":"4","key":"2058_CR6","doi-asserted-by":"publisher","first-page":"1401","DOI":"10.1021\/acs.jproteome.6b00618","volume":"16","author":"M Wen","year":"2017","unstructured":"Wen M, Zhang Z, Niu S, Sha H, Yang R, Yun Y, Lu H (2017) Deep-learning-based drug-target interaction prediction. J Proteome Res 16(4):1401\u20131409","journal-title":"J Proteome Res"},{"issue":"1","key":"2058_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-017-0209-z","volume":"9","author":"T He","year":"2017","unstructured":"He T, Heidemeyer M, Ban F, Cherkasov A, Ester M (2017) Simboost: a read-across approach for predicting drug-target binding affinities using gradient boosting machines. Journal of cheminformatics 9(1):1\u201314","journal-title":"Journal of cheminformatics"},{"issue":"17","key":"2058_CR8","doi-asserted-by":"publisher","first-page":"821","DOI":"10.1093\/bioinformatics\/bty593","volume":"34","author":"H \u00d6zt\u00fcrk","year":"2018","unstructured":"\u00d6zt\u00fcrk H, \u00d6zg\u00fcr A, Ozkirimli E (2018) Deepdta: deep drug-target binding affinity prediction. Bioinformatics 34(17):821\u2013829","journal-title":"Bioinformatics"},{"key":"2058_CR9","unstructured":"Shin B, Park S, Kang K, Ho JC (2019) Self-attention based molecule representation for predicting drug-target interaction. In: Machine Learning for Healthcare Conference, pp. 230\u2013248. PMLR"},{"issue":"2","key":"2058_CR10","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1093\/bib\/bbu010","volume":"16","author":"T Pahikkala","year":"2015","unstructured":"Pahikkala T, Airola A, Pietil\u00e4 S, Shakyawar S, Szwajda A, Tang J, Aittokallio T (2015) Toward more realistic drug-target interaction predictions. Brief Bioinform 16(2):325\u2013337","journal-title":"Brief Bioinform"},{"key":"2058_CR11","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.3389\/fgene.2019.01243","volume":"10","author":"L Zhao","year":"2020","unstructured":"Zhao L, Wang J, Pang L, Liu Y, Zhang J (2020) Gansdta: Predicting drug-target binding affinity using gans. Front Genet 10:1243","journal-title":"Front Genet"},{"key":"2058_CR12","doi-asserted-by":"crossref","unstructured":"Zhao Q, Xiao F, Yang M, Li Y, Wang J (2019) Attentiondta: prediction of drug\u2013target binding affinity using attention model. In: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 64\u201369. IEEE","DOI":"10.1109\/BIBM47256.2019.8983125"},{"issue":"8","key":"2058_CR13","doi-asserted-by":"publisher","first-page":"1140","DOI":"10.1093\/bioinformatics\/btaa921","volume":"37","author":"T Nguyen","year":"2021","unstructured":"Nguyen T, Le H, Quinn TP, Nguyen T, Le TD, Venkatesh S (2021) Graphdta: predicting drug-target binding affinity with graph neural networks. Bioinformatics 37(8):1140\u20131147","journal-title":"Bioinformatics"},{"issue":"4","key":"2058_CR14","doi-asserted-by":"publisher","first-page":"2208","DOI":"10.1109\/TCBB.2021.3077905","volume":"19","author":"Z Cheng","year":"2021","unstructured":"Cheng Z, Yan C, Wu F-X, Wang J (2021) Drug-target interaction prediction using multi-head self-attention and graph attention network. IEEE\/ACM Trans Comput Biol Bioinf 19(4):2208\u20132218","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"issue":"7","key":"2058_CR15","doi-asserted-by":"publisher","first-page":"1995","DOI":"10.1093\/bioinformatics\/btac035","volume":"38","author":"M Li","year":"2022","unstructured":"Li M, Lu Z, Wu Y, Li Y (2022) Bacpi: a bi-directional attention neural network for compound-protein interaction and binding affinity prediction. Bioinformatics 38(7):1995\u20132002","journal-title":"Bioinformatics"},{"issue":"1","key":"2058_CR16","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1021\/ci00057a005","volume":"28","author":"D Weininger","year":"1988","unstructured":"Weininger D (1988) Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. J Chem Inf Comput Sci 28(1):31\u201336","journal-title":"J Chem Inf Comput Sci"},{"issue":"12","key":"2058_CR17","doi-asserted-by":"publisher","first-page":"4369","DOI":"10.3390\/s18124369","volume":"18","author":"T Liu","year":"2018","unstructured":"Liu T, Bao J, Wang J, Zhang Y (2018) A hybrid cnn-lstm algorithm for online defect recognition of co2 welding. Sensors 18(12):4369","journal-title":"Sensors"},{"key":"2058_CR18","doi-asserted-by":"crossref","unstructured":"Wigington C, Stewart S, Davis B, Barrett B, Price B, Cohen S (2017) Data augmentation for recognition of handwritten words and lines using a cnn-lstm network. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 1:639\u2013645 . IEEE","DOI":"10.1109\/ICDAR.2017.110"},{"key":"2058_CR19","doi-asserted-by":"crossref","unstructured":"Wu C, Wu F, Chen Y, Wu S, Yuan Z, Huang Y (2018) Neural metaphor detecting with cnn-lstm model. In: Proceedings of the Workshop on Figurative Language Processing, pp. 110\u2013114","DOI":"10.18653\/v1\/W18-0913"},{"key":"2058_CR20","unstructured":"Landrum G (2010) Rdkit: open-source cheminformatics. release 2014.03. 1. arXiv preprint, 1908"},{"key":"2058_CR21","unstructured":"Zhao L, Akoglu L (2019) Pairnorm: Tackling oversmoothing in gnns. arXiv:1909.12223"},{"issue":"11","key":"2058_CR22","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.1038\/nbt.2017","volume":"29","author":"T Anastassiadis","year":"2011","unstructured":"Anastassiadis T, Deacon SW, Devarajan K, Ma H, Peterson JR (2011) Comprehensive assay of kinase catalytic activity reveals features of kinase inhibitor selectivity. Nat Biotechnol 29(11):1039\u20131045","journal-title":"Nat Biotechnol"},{"issue":"3","key":"2058_CR23","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1021\/ci400709d","volume":"54","author":"J Tang","year":"2014","unstructured":"Tang J, Szwajda A, Shakyawar S, Xu T, Hintsanen P, Wennerberg K, Aittokallio T (2014) Making sense of large-scale kinase inhibitor bioactivity data sets: a comparative and integrative analysis. J Chem Inf Model 54(3):735\u2013743","journal-title":"J Chem Inf Model"},{"issue":"12","key":"2058_CR24","doi-asserted-by":"publisher","first-page":"1071","DOI":"10.1002\/jcc.23231","volume":"34","author":"K Roy","year":"2013","unstructured":"Roy K, Chakraborty P, Mitra I, Ojha PK, Kar S, Das RN (2013) Some case studies on application of rm2 metrics for judging quality of quantitative structure-activity relationship predictions: emphasis on scaling of response data. J Comput Chem 34(12):1071\u20131082","journal-title":"J Comput Chem"},{"issue":"5","key":"2058_CR25","doi-asserted-by":"publisher","first-page":"1660","DOI":"10.3390\/molecules14051660","volume":"14","author":"P Pratim Roy","year":"2009","unstructured":"Pratim Roy P, Paul S, Mitra I, Roy K (2009) On two novel parameters for validation of predictive qsar models. Molecules 14(5):1660\u20131701","journal-title":"Molecules"},{"key":"2058_CR26","unstructured":"Lin X (2020) Deepgs: Deep representation learning of graphs and sequences for drug-target binding affinity prediction. arXiv:2003.13902"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-02058-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-023-02058-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-02058-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T14:49:42Z","timestamp":1721400582000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-023-02058-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,9]]},"references-count":26,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["2058"],"URL":"https:\/\/doi.org\/10.1007\/s13042-023-02058-3","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,9]]},"assertion":[{"value":"30 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}