{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:06:27Z","timestamp":1760832387142,"version":"build-2065373602"},"publisher-location":"Singapore","reference-count":32,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819534586","type":"print"},{"value":"9789819534593","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:00:00Z","timestamp":1760832000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:00:00Z","timestamp":1760832000000},"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":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-3459-3_5","type":"book-chapter","created":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T02:22:45Z","timestamp":1760754165000},"page":"66-80","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MMSA-DTA: Multi-modal Synergistic Attention for\u00a0Drug-Target Affinity Prediction"],"prefix":"10.1007","author":[{"given":"Jiajie","family":"Xing","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,19]]},"reference":[{"issue":"2","key":"5_CR1","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1080\/10408347.2014.881250","volume":"45","author":"A Olaru","year":"2015","unstructured":"Olaru, A., Bala, C., Jaffrezic-Renault, N., Aboul-Enein, H.Y.: Surface plasmon resonance (SPR) biosensors in pharmaceutical analysis. Crit. Rev. Anal. Chem. 45(2), 97\u2013105 (2015)","journal-title":"Crit. Rev. Anal. Chem."},{"key":"5_CR2","doi-asserted-by":"crossref","unstructured":"Cavalcanti, I.D.L., Junior, F.H.X., Magalh\u00e3es, N.S.S., Nogueira, M.C.D.B.L.: Isothermal titration calorimetry (ITC) as a promising tool in pharmaceutical nanotechnology. Int. J. Pharmaceutics 641, 123063 (2023)","DOI":"10.1016\/j.ijpharm.2023.123063"},{"issue":"1","key":"5_CR3","doi-asserted-by":"publisher","first-page":"2548","DOI":"10.1038\/s41467-025-57828-0","volume":"16","author":"Z Lu","year":"2025","unstructured":"Lu, Z., Song, G., Zhu, H., et al.: DTIAM: a unified framework for predicting drug-target interactions, binding affinities and drug mechanisms. Nat. Commun. 16(1), 2548 (2025)","journal-title":"Nat. Commun."},{"issue":"26","key":"5_CR4","doi-asserted-by":"publisher","first-page":"28485","DOI":"10.1021\/acsomega.4c02308","volume":"9","author":"L Han","year":"2024","unstructured":"Han, L., Kang, L., Guo, Q.: ImageDTA: a simple model for drug\u2013target binding affinity prediction. ACS Omega 9(26), 28485\u201328493 (2024)","journal-title":"ACS Omega"},{"key":"5_CR5","doi-asserted-by":"crossref","unstructured":"Hua, Y., Song, X., Feng, Z., et al.: MFR-DTA: a multi-functional and robust model for predicting drug\u2013target binding affinity and region. Bioinformatics 39(2), btad056 (2023)","DOI":"10.1093\/bioinformatics\/btad056"},{"key":"5_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.108003","volume":"244","author":"C Zhou","year":"2024","unstructured":"Zhou, C., Li, Z., Song, J., et al.: TransVAE-DTA: transformer and variational autoencoder network for drug-target binding affinity prediction. Comput. Methods Programs Biomed. 244, 108003 (2024)","journal-title":"Comput. Methods Programs Biomed."},{"issue":"8","key":"5_CR7","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, T.P., et al.: GraphDTA: predicting drug\u2013target binding affinity with graph neural networks. Bioinformatics 37(8), 1140\u20131147 (2021)","journal-title":"Bioinformatics"},{"issue":"1","key":"5_CR8","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1186\/s12859-024-05698-6","volume":"25","author":"H Qi","year":"2024","unstructured":"Qi, H., Yu, T., Yu, W., et al.: Drug\u2013target affinity prediction with extended graph learning-convolutional networks. BMC Bioinform. 25(1), 75 (2024)","journal-title":"BMC Bioinform."},{"issue":"3","key":"5_CR9","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. 13(3), 816\u2013833 (2022)","journal-title":"Chem. Sci."},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Tsui, L.I., Hsu, T.C., Lin, C.: NG-DTA: drug-target affinity prediction with n-gram molecular graphs. In: 2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1\u20134. IEEE (2023)","DOI":"10.1109\/EMBC40787.2023.10339968"},{"issue":"24","key":"5_CR11","doi-asserted-by":"publisher","first-page":"8005","DOI":"10.3390\/molecules28248005","volume":"28","author":"X Zeng","year":"2023","unstructured":"Zeng, X., Zhong, K.Y., Jiang, B., et al.: Fusing sequence and structural knowledge by heterogeneous models to accurately and interpretively predict drug\u2013target affinity. Molecules 28(24), 8005 (2023)","journal-title":"Molecules"},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Zhao, L., Wen, N., et al.: DataDTA: a multi-feature and dual-interaction aggregation framework for drug\u2013target binding affinity prediction. Bioinformatics 39(9), btad560 (2023)","DOI":"10.1093\/bioinformatics\/btad560"},{"key":"5_CR13","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. 169, 623\u2013636 (2024)","journal-title":"Neural Netw."},{"issue":"3","key":"5_CR14","doi-asserted-by":"publisher","first-page":"1223","DOI":"10.3390\/ijms26031223","volume":"26","author":"C Li","year":"2025","unstructured":"Li, C., Li, G.: DynHeter-DTA: dynamic heterogeneous graph representation for drug-target binding affinity prediction. Int. J. Mol. Sci. 26(3), 1223 (2025)","journal-title":"Int. J. Mol. Sci."},{"issue":"6","key":"5_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmb.2024.168843","volume":"437","author":"L Quan","year":"2025","unstructured":"Quan, L., Wu, J., Jiang, Y., et al.: DTA-GTOmega: enhancing drug-target binding affinity prediction with graph transformers using OmegaFold protein structures. J. Mol. Biol. 437(6), 168843 (2025)","journal-title":"J. Mol. Biol."},{"key":"5_CR16","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1186\/s12864-025-11234-4","volume":"26","author":"J Luo","year":"2025","unstructured":"Luo, J., Zhu, Z., Xu, Z., et al.: GS-DTA: integrating graph and sequence models for predicting drug-target binding affinity. BMC Genomics 26, 105 (2025)","journal-title":"BMC Genomics"},{"key":"5_CR17","first-page":"1","volume":"2025","author":"Y Liu","year":"2025","unstructured":"Liu, Y., Liu, Y., Yang, H., et al.: NTMFF-DTA: prediction of drug-target affinity based on network topology and multi-feature fusion. Interdisciplinary Sci. Comput. Life Sci. 2025, 1\u201313 (2025)","journal-title":"Interdisciplinary Sci. Comput. Life Sci."},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"Mahbub, S., Bayzid, M.S.: EGRET: edge aggregated graph attention networks and transfer learning improve protein\u2013protein interaction site prediction. Briefings Bioinform. 23(2), bbab578 (2022)","DOI":"10.1093\/bib\/bbab578"},{"key":"5_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2025.130052","volume":"637","author":"M Zheng","year":"2025","unstructured":"Zheng, M., Sun, G., Fan, Y.: MLC-DTA: drug-target affinity prediction based on multi-level contrastive learning and equivariant graph neural networks. Neurocomputing 637, 130052 (2025)","journal-title":"Neurocomputing"},{"issue":"3","key":"5_CR20","doi-asserted-by":"publisher","first-page":"405","DOI":"10.3390\/biom15030405","volume":"15","author":"K Debnath","year":"2025","unstructured":"Debnath, K., Rana, P., Ghosh, P.: GramSeq-DTA: a grammar-based drug-target affinity prediction approach fusing gene expression information. Biomolecules 15(3), 405 (2025)","journal-title":"Biomolecules"},{"issue":"8","key":"5_CR21","doi-asserted-by":"publisher","first-page":"4544","DOI":"10.1109\/JBHI.2024.3350666","volume":"28","author":"X Yang","year":"2024","unstructured":"Yang, X., Yang, G., Chu, J.: GraphCL-DTA: a graph contrastive learning with molecular semantics for drug-target binding affinity prediction. IEEE J. Biomed. Health Inform. 28(8), 4544\u20134552 (2024)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"5_CR22","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 92, 106110 (2024)","journal-title":"Biomed. Signal Process. Control"},{"key":"5_CR23","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.patcog.2018.01.020","volume":"79","author":"Z Tu","year":"2018","unstructured":"Tu, Z., Xie, W., Qin, Q., et al.: Multi-stream CNN: learning representations based on human-related regions for action recognition. Pattern Recogn. 79, 32\u201343 (2018)","journal-title":"Pattern Recogn."},{"key":"5_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121882","volume":"238","author":"J Zhang","year":"2024","unstructured":"Zhang, J., Liu, Z., Pan, Y., et al.: IMAEN: an interpretable molecular augmentation model for drug\u2013target interaction prediction. Expert Syst. Appl. 238, 121882 (2024)","journal-title":"Expert Syst. Appl."},{"key":"5_CR25","doi-asserted-by":"publisher","first-page":"2020","DOI":"10.1021\/acsomega.4c08048","volume":"10","author":"Z Li","year":"2025","unstructured":"Li, Z., Zeng, Y., Jiang, M., et al.: Deep Drug-Target Binding Affinity Prediction Base on Multiple Feature Extraction and Fusion. ACS Omega 10, 2020\u20132032 (2025)","journal-title":"ACS Omega"},{"issue":"1","key":"5_CR26","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/ac3ffb","volume":"3","author":"R Irwin","year":"2022","unstructured":"Irwin, R., Dimitriadis, S., He, J., Bjerrum, E.J.: Chemformer: a pre-trained transformer for computational chemistry. Machine Learn. Sci. Technol. 3(1), 015022 (2022)","journal-title":"Machine Learn. Sci. Technol."},{"key":"5_CR27","doi-asserted-by":"crossref","unstructured":"Hie, B., Candido, S., Lin, Z., Kabeli, O., Rao, R., Smetanin, N., et al.: A high-level programming language for generative protein design. BioRxiv (2022)","DOI":"10.1101\/2022.12.21.521526"},{"key":"5_CR28","doi-asserted-by":"crossref","unstructured":"Li, Z., Ren, P., Yang, H., et al.: TEFDTA: a transformer encoder and fingerprint representation combined prediction method for bonded and non-bonded drug\u2013target affinities. Bioinformatics 40(1), btad778 (2024)","DOI":"10.1093\/bioinformatics\/btad778"},{"key":"5_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiolchem.2023.107982","volume":"108","author":"C Tian","year":"2024","unstructured":"Tian, C., Wang, L., Cui, Z., et al.: GTAMP-DTA: graph transformer combined with attention mechanism for drug-target binding affinity prediction. Comput. Biol. Chem. 108, 107982 (2024)","journal-title":"Comput. Biol. Chem."},{"issue":"11","key":"5_CR30","doi-asserted-by":"publisher","first-page":"1046","DOI":"10.1038\/nbt.1990","volume":"29","author":"MI Davis","year":"2011","unstructured":"Davis, M.I., Hunt, J.P., Herrgard, S., et al.: Comprehensive analysis of kinase inhibitor selectivity. Nat. Biotechnol. 29(11), 1046\u20131051 (2011)","journal-title":"Nat. Biotechnol."},{"issue":"3","key":"5_CR31","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. 54(3), 735\u2013743 (2014)","journal-title":"J. Chem. Inf. Model."},{"key":"5_CR32","unstructured":"Srivastava, R. K., Greff, K., Schmidhuber, J.: Highway networks. arXiv preprint arXiv:1505.00387 (2015)"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-3459-3_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T02:22:49Z","timestamp":1760754169000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-3459-3_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,19]]},"ISBN":["9789819534586","9789819534593"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3459-3_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,19]]},"assertion":[{"value":"19 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kyoto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2025.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}