{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T20:07:10Z","timestamp":1783973230181,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":28,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819234912","type":"print"},{"value":"9789819234929","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"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":[[2027]]},"DOI":"10.1007\/978-981-92-3492-9_35","type":"book-chapter","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T19:40:32Z","timestamp":1783971632000},"page":"421-433","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["KAN-DTA: Interaction-Aware DTA Prediction via Cross-Modal"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-8273-1506","authenticated-orcid":false,"given":"Tao","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7319-3868","authenticated-orcid":false,"given":"Pengfei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8133-8490","authenticated-orcid":false,"given":"QiXuan","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4834-9236","authenticated-orcid":false,"given":"JianGuang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7151-8995","authenticated-orcid":false,"given":"Yunyun","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,14]]},"reference":[{"issue":"7958","key":"35_CR1","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1038\/s41586-023-05905-z","volume":"616","author":"AV Sadybekov","year":"2023","unstructured":"Sadybekov, A.V., Katritch, V.: Computational approaches streamlining drug discovery. Nature 616(7958), 673\u2013685 (2023)","journal-title":"Nature"},{"key":"35_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2025.110438","volume":"196","author":"A Vefghi","year":"2025","unstructured":"Vefghi, A., Rahmati, Z., Akbari, M.: Drug-target interaction\/affinity prediction: deep learning models and advances review. Comput. Biol. Med. 196, 110438 (2025)","journal-title":"Comput. Biol. Med."},{"issue":"3","key":"35_CR3","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1011036","volume":"19","author":"M Kalemati","year":"2023","unstructured":"Kalemati, M., Zamani Emani, M., Koohi, S.: BiComp-DTA: drug-target binding affinity prediction through complementary biological-related and compression-based featurization approach. PLoS Comput. Biol. 19(3), e1011036 (2023)","journal-title":"PLoS Comput. Biol."},{"issue":"15","key":"35_CR4","doi-asserted-by":"publisher","first-page":"10261","DOI":"10.1039\/D3RA00281K","volume":"13","author":"T Voitsitskyi","year":"2023","unstructured":"Voitsitskyi, T., Stratiichuk, R., Koleiev, I., et al.: 3DProtDTA: a deep learning model for drug-target affinity prediction based on residue-level protein graphs. RSC Adv. 13(15), 10261\u201310272 (2023)","journal-title":"RSC Adv."},{"key":"35_CR5","doi-asserted-by":"crossref","unstructured":"Kumar, R., Romano, J.D., Ritchie, M.D.: CASTER-DTA: equivariant graph neural networks for predicting drug\u2013target affinity. Brief. Bioinform. 26(5), bbaf554 (2025)","DOI":"10.1093\/bib\/bbaf554"},{"key":"35_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2024.102983","volume":"157","author":"Y Liu","year":"2024","unstructured":"Liu, Y., Xia, X., Gong, Y., et al.: SSR-DTA: substructure-aware multi-layer graph neural networks for drug\u2013target binding affinity prediction. Artif. Intell. Med. 157, 102983 (2024)","journal-title":"Artif. Intell. Med."},{"key":"35_CR7","doi-asserted-by":"crossref","unstructured":"Wang, K., Zhou, R., Tang, J., et al.: GraphscoreDTA: optimized graph neural network for protein\u2013ligand binding affinity prediction. Bioinformatics 39(6), btad340 (2023)","DOI":"10.1093\/bioinformatics\/btad340"},{"key":"35_CR8","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"},{"issue":"1","key":"35_CR9","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1186\/s13321-022-00591-x","volume":"14","author":"J Wang","year":"2022","unstructured":"Wang, J., Wen, N.F., Wang, C., et al.: ELECTRA-DTA: a new compound-protein binding affinity prediction model based on the contextualized sequence encoding. J. Cheminform. 14(1), 14 (2022)","journal-title":"J. Cheminform."},{"key":"35_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122334","volume":"238","author":"NRC Monteiro","year":"2024","unstructured":"Monteiro, N.R.C., Oliveira, J.L., Arrais, J.P.: TAG-DTA: binding-region-guided strategy to predict drug-target affinity using transformers. Expert Syst. Appl. 238, 122334 (2024)","journal-title":"Expert Syst. Appl."},{"issue":"5","key":"35_CR11","doi-asserted-by":"publisher","first-page":"2151","DOI":"10.1109\/TAI.2023.3314405","volume":"5","author":"Y Ding","year":"2023","unstructured":"Ding, Y., Guo, F., Tiwari, P., et al.: Identification of drug\u2013side-effect association via multiview semi supervised sparse model. IEEE Trans. Artif. Intell. 5(5), 2151\u20132162 (2023)","journal-title":"IEEE Trans. Artif. Intell."},{"key":"35_CR12","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":"35_CR13","doi-asserted-by":"publisher","first-page":"1579","DOI":"10.1109\/JBHI.2023.3334239","volume":"29","author":"X Bi","year":"2023","unstructured":"Bi, X., Zhang, S., Ma, W., et al.: HiSIF-DTA: a hierarchical semantic information fusion framework for drug-target affinity prediction. IEEE J. Biomed. Health Inform. 29(3), 1579\u20131590 (2023)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"35_CR14","doi-asserted-by":"crossref","unstructured":"Meng, Z., Meng, Z., Yuan, K., et al.: FusionDTI: fine-grained binding discovery with token-level fusion for drug-target interaction. arXiv preprint (2024)","DOI":"10.18653\/v1\/2025.findings-emnlp.237"},{"key":"35_CR15","doi-asserted-by":"crossref","unstructured":"Zhai, X., Wang, C., Wang, R., et al.: Blend the separated: mixture of synergistic experts for data-scarcity drug-target interaction prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, no. 21, pp. 22336\u201322344 (2025)","DOI":"10.1609\/aaai.v39i21.34389"},{"issue":"1","key":"35_CR16","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":"1","key":"35_CR17","doi-asserted-by":"publisher","first-page":"6436","DOI":"10.1038\/s41467-025-61745-7","volume":"16","author":"Q Zhao","year":"2025","unstructured":"Zhao, Q., Zhao, H., Guo, L., et al.: ColdstartCPI: induced-fit theory-guided DTI predictive model with improved generalization performance. Nat. Commun. 16(1), 6436 (2025)","journal-title":"Nat. Commun."},{"key":"35_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2025.113699","volume":"320","author":"J Wang","year":"2025","unstructured":"Wang, J., Ding, P., Zhu, Y., et al.: MSN-DTA: a multi-scale node adaptive graph neural network for interpretable drug-target binding affinity prediction. Knowl. Based Syst. 320, 113699 (2025)","journal-title":"Knowl. Based Syst."},{"key":"35_CR19","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":"35_CR20","doi-asserted-by":"crossref","unstructured":"Li, M., Wang, Y., Guo, P., et al.: HiF-DTA: hierarchical feature learning network for drug-target affinity prediction. arXiv preprint arXiv:2510.27281 (2025)","DOI":"10.1109\/BIBM66473.2025.11356849"},{"issue":"10","key":"35_CR21","doi-asserted-by":"publisher","first-page":"5126","DOI":"10.3390\/ijms25105126","volume":"25","author":"X Tang","year":"2024","unstructured":"Tang, X., Lei, X., Zhang, Y.: Prediction of drug-target affinity using attention neural network. Int. J. Mol. Sci. 25(10), 5126 (2024)","journal-title":"Int. J. Mol. Sci."},{"key":"35_CR22","doi-asserted-by":"crossref","unstructured":"Xu, F., Xiao, Q., Zhu, Y., et al.: Drug-target binding affinity prediction by combination graph neural network and BiLSTM. In: 2024 IEEE 7th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), vol. 7, pp. 1012\u20131016. IEEE (2024)","DOI":"10.1109\/IAEAC59436.2024.10503589"},{"issue":"4","key":"35_CR23","doi-asserted-by":"publisher","first-page":"1558","DOI":"10.1049\/cit2.12194","volume":"8","author":"Z Zhu","year":"2023","unstructured":"Zhu, Z., Yao, Z., Qi, G., et al.: Associative learning mechanism for drug-target interaction prediction. CAAI Trans. Intell. Technol. 8(4), 1558\u20131577 (2023)","journal-title":"CAAI Trans. Intell. Technol."},{"key":"35_CR24","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.ymeth.2024.11.010","volume":"234","author":"C He","year":"2025","unstructured":"He, C., Zhao, Z., Wang, X., et al.: Exploring drug-target interaction prediction on cold-start scenarios via meta-learning-based graph transformer. Methods 234, 10\u201320 (2025)","journal-title":"Methods"},{"issue":"1","key":"35_CR25","doi-asserted-by":"publisher","first-page":"5021","DOI":"10.1038\/s41467-025-59917-6","volume":"16","author":"PM Shah","year":"2025","unstructured":"Shah, P.M., Zhu, H., Lu, Z., et al.: DeepDTAGen: a multitask deep learning framework for drug-target affinity prediction and target-aware drugs generation. Nat. Commun. 16(1), 5021 (2025)","journal-title":"Nat. Commun."},{"key":"35_CR26","doi-asserted-by":"crossref","unstructured":"Pei, Q., Wu, L., Zhu, J., et al.: Breaking the barriers of data scarcity in drug\u2013target affinity prediction. Brief. Bioinform. 24(6), bbad386 (2023)","DOI":"10.1093\/bib\/bbad386"},{"issue":"1","key":"35_CR27","doi-asserted-by":"publisher","first-page":"6234","DOI":"10.1038\/s41467-023-41454-9","volume":"14","author":"H Zhu","year":"2023","unstructured":"Zhu, H., Zhou, R., Cao, D., et al.: A pharmacophore-guided deep learning approach for bioactive molecular generation. Nat. Commun. 14(1), 6234 (2023)","journal-title":"Nat. Commun."},{"key":"35_CR28","doi-asserted-by":"crossref","unstructured":"Sun, X., Jia, X., Lu, Z., et al.: Drug repositioning with adaptive graph convolutional networks. Bioinformatics 40(1), btad748 (2024)","DOI":"10.1093\/bioinformatics\/btad748"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-3492-9_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T19:40:35Z","timestamp":1783971635000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-3492-9_35"}},"subtitle":["Attention and Kolmogorov\u2013Arnold Networks"],"short-title":[],"issued":{"date-parts":[[2026,7,14]]},"ISBN":["9789819234912","9789819234929"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-3492-9_35","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,14]]},"assertion":[{"value":"14 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Toronto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2026a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2026\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}