{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:56:06Z","timestamp":1757620566497,"version":"3.44.0"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819506941"},{"type":"electronic","value":"9789819506958"}],"license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"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\/978-981-95-0695-8_24","type":"book-chapter","created":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T12:53:54Z","timestamp":1753966434000},"page":"295-306","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Drug-Target Interaction Prediction via\u00a0Substructure Similarity-Guided Denoising and\u00a0Hierarchical Feature Fusion"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8962-093X","authenticated-orcid":false,"given":"Minzhu","family":"Xie","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2264-7166","authenticated-orcid":false,"given":"Dongze","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6551-0434","authenticated-orcid":false,"given":"Yabin","family":"Kuang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,8,1]]},"reference":[{"issue":"2","key":"24_CR1","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1038\/s42256-022-00605-1","volume":"5","author":"P Bai","year":"2023","unstructured":"Bai, P., Miljkovi\u0107, F., John, B., Lu, H.: Interpretable bilinear attention network with domain adaptation improves drug-target prediction. Nat. Mach. Intell. 5(2), 126\u2013136 (2023)","journal-title":"Nat. Mach. Intell."},{"key":"24_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-020-00456-1","volume":"12","author":"AP Bento","year":"2020","unstructured":"Bento, A.P., et al.: An open source chemical structure curation pipeline using RDKit. J. Cheminformatics 12, 1\u201316 (2020)","journal-title":"J. Cheminformatics"},{"key":"24_CR3","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45, 5\u201332 (2001)","journal-title":"Mach. Learn."},{"issue":"17","key":"24_CR4","doi-asserted-by":"publisher","first-page":"4153","DOI":"10.1093\/bioinformatics\/btac485","volume":"38","author":"Z Cheng","year":"2022","unstructured":"Cheng, Z., Zhao, Q., Li, Y., Wang, J.: IIFDTI: predicting drug-target interactions through interactive and independent features based on attention mechanism. Bioinformatics 38(17), 4153\u20134161 (2022)","journal-title":"Bioinformatics"},{"key":"24_CR5","doi-asserted-by":"crossref","unstructured":"Gene Ontology Consortium: The gene ontology project in 2008. Nucleic Acids Res. 36(suppl_1), D440\u2013D444 (2008)","DOI":"10.1093\/nar\/gkm883"},{"key":"24_CR6","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1023\/A:1022627411411","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273\u2013297 (1995)","journal-title":"Mach. Learn."},{"issue":"2","key":"24_CR7","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1111\/j.2517-6161.1958.tb00292.x","volume":"20","author":"DR Cox","year":"1958","unstructured":"Cox, D.R.: The regression analysis of binary sequences. J. R. Stat. Soc. Ser. B Stat Methodol. 20(2), 215\u2013232 (1958)","journal-title":"J. R. Stat. Soc. Ser. B Stat Methodol."},{"issue":"11","key":"24_CR8","doi-asserted-by":"publisher","first-page":"1046","DOI":"10.1038\/nbt.1990","volume":"29","author":"MI Davis","year":"2011","unstructured":"Davis, M.I., et al.: Comprehensive analysis of kinase inhibitor selectivity. Nat. Biotechnol. 29(11), 1046\u20131051 (2011)","journal-title":"Nat. Biotechnol."},{"issue":"10","key":"24_CR9","doi-asserted-by":"publisher","first-page":"7112","DOI":"10.1109\/TPAMI.2021.3095381","volume":"44","author":"A Elnaggar","year":"2021","unstructured":"Elnaggar, A., et al.: ProtTrans: toward understanding the language of life through self-supervised learning. IEEE Trans. Pattern Anal. Mach. Intell. 44(10), 7112\u20137127 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"4","key":"24_CR10","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.: Computational prediction of drug-target interactions using chemogenomic approaches: an empirical survey. Brief. Bioinform. 20(4), 1337\u20131357 (2019)","journal-title":"Brief. Bioinform."},{"issue":"6","key":"24_CR11","doi-asserted-by":"publisher","first-page":"830","DOI":"10.1093\/bioinformatics\/btaa880","volume":"37","author":"K Huang","year":"2021","unstructured":"Huang, K., Xiao, C., Glass, L.M., Sun, J.: MolTrans: molecular interaction transformer for drug-target interaction prediction. Bioinformatics 37(6), 830\u2013836 (2021)","journal-title":"Bioinformatics"},{"issue":"1","key":"24_CR12","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1021\/acs.jcim.7b00616","volume":"58","author":"S Jaeger","year":"2018","unstructured":"Jaeger, S., Fulle, S., Turk, S.: Mol2vec: unsupervised machine learning approach with chemical intuition. J. Chem. Inf. Model. 58(1), 27\u201335 (2018)","journal-title":"J. Chem. Inf. Model."},{"issue":"6","key":"24_CR13","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1007129","volume":"15","author":"I Lee","year":"2019","unstructured":"Lee, I., Keum, J., Nam, H.: DeepConv-DTI: prediction of drug-target interactions via deep learning with convolution on protein sequences. PLoS Comput. Biol. 15(6), e1007129 (2019)","journal-title":"PLoS Comput. Biol."},{"issue":"17","key":"24_CR14","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-target binding affinity prediction. Bioinformatics 34(17), i821\u2013i829 (2018)","journal-title":"Bioinformatics"},{"issue":"5","key":"24_CR15","doi-asserted-by":"publisher","first-page":"693","DOI":"10.1093\/bioinformatics\/btaa858","volume":"37","author":"AS Rifaioglu","year":"2021","unstructured":"Rifaioglu, A.S., Cetin Atalay, R., Cansen Kahraman, D., Do\u011fan, T., Martin, M., Atalay, V.: MDeePred: novel multi-channel protein featurization for deep learning-based binding affinity prediction in drug discovery. Bioinformatics 37(5), 693\u2013704 (2021)","journal-title":"Bioinformatics"},{"issue":"3","key":"24_CR16","doi-asserted-by":"publisher","first-page":"1006","DOI":"10.1039\/C5MB00650C","volume":"12","author":"N Shaikh","year":"2016","unstructured":"Shaikh, N., Sharma, M., Garg, P.: An improved approach for predicting drug-target interaction: proteochemometrics to molecular docking. Mol. BioSyst. 12(3), 1006\u20131014 (2016)","journal-title":"Mol. BioSyst."},{"key":"24_CR17","doi-asserted-by":"crossref","unstructured":"Song, W., Xu, L., Han, C., Tian, Z., Zou, Q.: Drug\u2013target interaction predictions with multi-view similarity network fusion strategy and deep interactive attention mechanism. Bioinformatics 40(6), btae346 (2024)","DOI":"10.1093\/bioinformatics\/btae346"},{"key":"24_CR18","doi-asserted-by":"crossref","unstructured":"Tian, Z., Peng, X., Fang, H., Zhang, W., Dai, Q., Ye, Y.: MHADTI: predicting drug\u2013target interactions via multiview heterogeneous information network embedding with hierarchical attention mechanisms. Briefings Bioinf. 23(6), bbac434 (2022)","DOI":"10.1093\/bib\/bbac434"},{"issue":"2","key":"24_CR19","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1093\/bioinformatics\/bty535","volume":"35","author":"M Tsubaki","year":"2019","unstructured":"Tsubaki, M., Tomii, K., Sese, J.: Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences. Bioinformatics 35(2), 309\u2013318 (2019)","journal-title":"Bioinformatics"},{"issue":"15","key":"24_CR20","doi-asserted-by":"publisher","first-page":"10261","DOI":"10.1039\/D3RA00281K","volume":"13","author":"T Voitsitskyi","year":"2023","unstructured":"Voitsitskyi, T., 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."},{"issue":"7","key":"24_CR21","doi-asserted-by":"publisher","first-page":"2096","DOI":"10.1039\/C5MB00306G","volume":"11","author":"Y Yeu","year":"2015","unstructured":"Yeu, Y., Yoon, Y., Park, S.: Protein localization vector propagation: a method for improving the accuracy of drug repositioning. Mol. BioSyst. 11(7), 2096\u20132102 (2015)","journal-title":"Mol. BioSyst."},{"key":"24_CR22","doi-asserted-by":"crossref","unstructured":"Zhang, Q., et al.: FMCA-DTI: a fragment-oriented method based on a multihead cross attention mechanism to improve drug\u2013target interaction prediction. Bioinformatics 40(6), btae347 (2024)","DOI":"10.1093\/bioinformatics\/btae347"},{"issue":"13","key":"24_CR23","doi-asserted-by":"publisher","first-page":"i457","DOI":"10.1093\/bioinformatics\/bty294","volume":"34","author":"M Zitnik","year":"2018","unstructured":"Zitnik, M., Agrawal, M., Leskovec, J.: Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics 34(13), i457\u2013i466 (2018)","journal-title":"Bioinformatics"}],"container-title":["Lecture Notes in Computer Science","Bioinformatics Research and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-0695-8_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T09:34:25Z","timestamp":1757324065000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-0695-8_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,1]]},"ISBN":["9789819506941","9789819506958"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-0695-8_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,8,1]]},"assertion":[{"value":"1 August 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISBRA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Bioinformatics Research and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Helsinki","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Finland","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":"3 August 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 August 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":"isbra2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.helsinki.fi\/en\/conferences\/isbra2025","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}