{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T18:02:11Z","timestamp":1792864931319,"version":"4.5.0"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T00:00:00Z","timestamp":1738627200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T00:00:00Z","timestamp":1738627200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Genomics"],"DOI":"10.1186\/s12864-025-11234-4","type":"journal-article","created":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T09:28:11Z","timestamp":1738661291000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["GS-DTA: integrating graph and sequence models for predicting drug-target binding affinity"],"prefix":"10.1186","volume":"26","author":[{"given":"Junwei","family":"Luo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziguang","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenhan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanle","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjing","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiquan","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,4]]},"reference":[{"issue":"6","key":"11234_CR1","doi-asserted-by":"publisher","first-page":"1239","DOI":"10.1111\/j.1476-5381.2010.01127.x","volume":"162","author":"JP Hughes","year":"2011","unstructured":"Hughes JP, Rees S, Kalindjian SB, et al. Principles of early drug discovery. Br J Pharmacol. 2011;162(6):1239\u201349.","journal-title":"Br J Pharmacol"},{"issue":"1","key":"11234_CR2","doi-asserted-by":"publisher","first-page":"2694","DOI":"10.3762\/bjoc.12.267","volume":"12","author":"SP Leelananda","year":"2016","unstructured":"Leelananda SP, Lindert S. Computational methods in drug discovery. Beilstein J Org Chem. 2016;12(1):2694\u2013718.","journal-title":"Beilstein J Org Chem"},{"issue":"6","key":"11234_CR3","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1038\/nm.3595","volume":"20","author":"SM Strittmatter","year":"2014","unstructured":"Strittmatter SM. Overcoming drug development bottlenecks with repurposing: old drugs learn new tricks. Nat Med. 2014;20(6):590\u20131.","journal-title":"Nat Med"},{"issue":"3","key":"11234_CR4","doi-asserted-by":"publisher","first-page":"bbaa205","DOI":"10.1093\/bib\/bbaa205","volume":"22","author":"Y Chu","year":"2021","unstructured":"Chu Y, Shan X, Chen T, et al. DTI-MLCD: predicting drug-target interactions using multi-label learning with community detection method. Briefings in bioinformatics. 2021;22(3):bbaa205.","journal-title":"Briefings in bioinformatics"},{"key":"11234_CR5","doi-asserted-by":"crossref","unstructured":"Weng Y, Lin C, Zeng X, et al. Drug target interaction prediction using multi-task learning and co-attention[C]\/\/2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE. 2019:528\u2013533.","DOI":"10.1109\/BIBM47256.2019.8983254"},{"issue":"8","key":"11234_CR6","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, et al. GraphDTA: predicting drug\u2013target binding affinity with graph neural networks. Bioinformatics. 2021;37(8):1140\u20137.","journal-title":"Bioinformatics"},{"issue":"17","key":"11234_CR7","doi-asserted-by":"publisher","first-page":"i821","DOI":"10.1093\/bioinformatics\/bty593","volume":"34","author":"H \u00d6zt\u00fcrk","year":"2018","unstructured":"\u00d6zt\u00fcrk H, \u00d6zg\u00fcr A, Ozkirimli E. DeepDTA: deep drug\u2013target binding affinity prediction. Bioinformatics. 2018;34(17):i821\u20139.","journal-title":"Bioinformatics"},{"key":"11234_CR8","doi-asserted-by":"publisher","unstructured":"Dehghan A , Abbasi K , Razzaghi P ,et al.CCL-DTI: contributing the contrastive loss in drug\u2013target interaction prediction. BMC Bioinformatics. 2024;25(1). https:\/\/doi.org\/10.1186\/s12859-024-05671-3.","DOI":"10.1186\/s12859-024-05671-3"},{"issue":"7","key":"11234_CR9","doi-asserted-by":"publisher","first-page":"2577","DOI":"10.1021\/acs.jcim.3c01486","volume":"64","author":"F Rafiei","year":"2024","unstructured":"Rafiei F, Zeraati H, Abbasi K, et al. CFSSynergy: combining feature-based and similarity-based methods for drug synergy prediction. J Chem Inf Model. 2024;64(7):2577\u201385.","journal-title":"J Chem Inf Model"},{"key":"11234_CR10","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, et al. SimBoost: a read-across approach for predicting drug\u2013target binding affinities using gradient boosting machines. Journal of cheminformatics. 2017;9:1\u201314.","journal-title":"Journal of cheminformatics"},{"issue":"13","key":"11234_CR11","doi-asserted-by":"publisher","first-page":"i509","DOI":"10.1093\/bioinformatics\/bty277","volume":"34","author":"A Cichonska","year":"2018","unstructured":"Cichonska A, Pahikkala T, Szedmak S, et al. Learning with multiple pairwise kernels for drug bioactivity prediction. Bioinformatics. 2018;34(13):i509\u201318.","journal-title":"Bioinformatics"},{"key":"11234_CR12","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber J. Deep learning in neural networks: An overview. Neural Netw. 2015;61:85\u2013117.","journal-title":"Neural Netw"},{"key":"11234_CR13","doi-asserted-by":"crossref","unstructured":"\u00d6zt\u00fcrk H, Ozkirimli E, \u00d6zg\u00fcr A. WideDTA: prediction of drug-target binding affinity. arXiv preprint arXiv:1902.04166, 2019.","DOI":"10.1093\/bioinformatics\/bty593"},{"issue":"17","key":"11234_CR14","doi-asserted-by":"publisher","first-page":"4633","DOI":"10.1093\/bioinformatics\/btaa544","volume":"36","author":"K Abbasi","year":"2020","unstructured":"Abbasi K, Razzaghi P, Poso A, et al. DeepCDA: deep cross-domain compound\u2013protein affinity prediction through LSTM and convolutional neural networks. Bioinformatics. 2020;36(17):4633\u201342.","journal-title":"Bioinformatics"},{"issue":"7","key":"11234_CR15","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1162\/neco_a_01199","volume":"31","author":"Y Yu","year":"2019","unstructured":"Yu Y, Si X, Hu C, et al. A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput. 2019;31(7):1235\u201370.","journal-title":"Neural Comput"},{"issue":"15","key":"11234_CR16","doi-asserted-by":"publisher","first-page":"8453","DOI":"10.3390\/ijms23158453","volume":"23","author":"A Ghimire","year":"2022","unstructured":"Ghimire A, Tayara H, Xuan Z, et al. CSatDTA: prediction of drug\u2013target binding affinity using convolution model with self-attention. Int J Mol Sci. 2022;23(15):8453.","journal-title":"Int J Mol Sci"},{"key":"11234_CR17","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","volume":"1","author":"J Zhou","year":"2020","unstructured":"Zhou J, Cui G, Hu S, et al. Graph neural networks: A review of methods and applications. AI open. 2020;1:57\u201381.","journal-title":"AI open"},{"key":"11234_CR18","unstructured":"Xu K, Hu W, Leskovec J, et al. How powerful are graph neural networks?. arXiv preprint arXiv:1810.00826, 2018."},{"issue":"3","key":"11234_CR19","doi-asserted-by":"publisher","first-page":"816","DOI":"10.1039\/D1SC05180F","volume":"13","author":"Z Yang","year":"2022","unstructured":"Yang Z, Zhong W, Zhao L, et al. MGraphDTA: deep multiscale graph neural network for explainable drug\u2013target binding affinity prediction. Chem Sci. 2022;13(3):816\u201333.","journal-title":"Chem Sci"},{"key":"11234_CR20","doi-asserted-by":"crossref","unstructured":"Mukherjee S, Ghosh M, Basuchowdhuri P. DeepGLSTM: deep graph convolutional network and LSTM based approach for predicting drug-target binding affinity[C]\/\/Proceedings of the 2022 SIAM international conference on data mining (SDM). Soc Industr Appl Mathematics, 2022: 729\u2013737.","DOI":"10.1137\/1.9781611977172.82"},{"issue":"35","key":"11234_CR21","doi-asserted-by":"publisher","first-page":"20701","DOI":"10.1039\/D0RA02297G","volume":"10","author":"M Jiang","year":"2020","unstructured":"Jiang M, Li Z, Zhang S, et al. Drug\u2013target affinity prediction using graph neural network and contact maps. RSC Adv. 2020;10(35):20701\u201312.","journal-title":"RSC Adv"},{"key":"11234_CR22","doi-asserted-by":"crossref","unstructured":"Xu S, Wang R. OdinDTA: Combining Mutual Attention and Pre-training for Drug-target Affinity Prediction[C]\/\/2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE. 2023: 680\u2013687.","DOI":"10.1109\/ICTAI59109.2023.00106"},{"issue":"1","key":"11234_CR23","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1186\/s12859-023-05497-5","volume":"24","author":"H Zhai","year":"2023","unstructured":"Zhai H, Hou H, Luo J, et al. DGDTA: dynamic graph attention network for predicting drug\u2013target binding affinity. BMC Bioinformatics. 2023;24(1):367.","journal-title":"BMC Bioinformatics"},{"key":"11234_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106110","volume":"92","author":"L Zhang","year":"2024","unstructured":"Zhang L, Zeng W, Chen J, et al. GDilatedDTA: Graph dilation convolution strategy for drug target binding affinity prediction. Biomed Signal Process Control. 2024;92: 106110.","journal-title":"Biomed Signal Process Control"},{"key":"11234_CR25","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1016\/j.neunet.2023.11.018","volume":"169","author":"H Wu","year":"2024","unstructured":"Wu H, Liu J, Jiang T, et al. AttentionMGT-DTA: A multi-modal drug-target affinity prediction using graph transformer and attention mechanism. Neural Netw. 2024;169:623\u201336.","journal-title":"Neural Netw"},{"issue":"11","key":"11234_CR26","doi-asserted-by":"publisher","first-page":"1046","DOI":"10.1038\/nbt.1990","volume":"29","author":"MI Davis","year":"2011","unstructured":"Davis MI, Hunt JP, Herrgard S, et al. Comprehensive analysis of kinase inhibitor selectivity. Nat Biotechnol. 2011;29(11):1046\u201351.","journal-title":"Nat Biotechnol"},{"issue":"3","key":"11234_CR27","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, et al. Making sense of large-scale kinase inhibitor bioactivity data sets: a comparative and integrative analysis. J Chem Inf Model. 2014;54(3):735\u201343.","journal-title":"J Chem Inf Model"},{"key":"11234_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-020-00456-1","volume":"12","author":"AP Bento","year":"2020","unstructured":"Bento AP, Hersey A, F\u00e9lix E, et al. An open source chemical structure curation pipeline using RDKit. Journal of Cheminformatics. 2020;12:1\u201316.","journal-title":"Journal of Cheminformatics"},{"issue":"31.10","key":"11234_CR29","first-page":"5281","volume":"8","author":"G Landrum","year":"2013","unstructured":"Landrum G. RDKit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum. 2013;8(31.10):5281.","journal-title":"Greg Landrum."}],"container-title":["BMC Genomics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12864-025-11234-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12864-025-11234-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12864-025-11234-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T09:28:16Z","timestamp":1738661296000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcgenomics.biomedcentral.com\/articles\/10.1186\/s12864-025-11234-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,4]]},"references-count":29,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["11234"],"URL":"https:\/\/doi.org\/10.1186\/s12864-025-11234-4","relation":{},"ISSN":["1471-2164"],"issn-type":[{"value":"1471-2164","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,4]]},"assertion":[{"value":"14 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 February 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"105"}}