{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T21:02:15Z","timestamp":1784408535145,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":18,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819234028","type":"print"},{"value":"9789819234035","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T00:00:00Z","timestamp":1784419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T00:00:00Z","timestamp":1784419200000},"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\/978-981-92-3403-5_30","type":"book-chapter","created":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T20:07:15Z","timestamp":1784405235000},"page":"381-392","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Gated Semantic-Graph Network for Accurate Drug-Drug Interaction Prediction"],"prefix":"10.1007","author":[{"given":"Yilou","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoyi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengkun","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,19]]},"reference":[{"issue":"9","key":"30_CR1","doi-asserted-by":"publisher","first-page":"2147","DOI":"10.1038\/nprot.2014.151","volume":"9","author":"S Vilar","year":"2014","unstructured":"Vilar, S., et al.: Similarity-based modeling in large-scale prediction of drug-drug interactions. Nat. Protoc. 9(9), 2147\u20132163 (2014)","journal-title":"Nat. Protoc."},{"issue":"13","key":"30_CR2","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"},{"issue":"6","key":"30_CR3","doi-asserted-by":"publisher","first-page":"1066","DOI":"10.1136\/amiajnl-2012-000935","volume":"19","author":"S Vilar","year":"2012","unstructured":"Vilar, S., Harpaz, R., Uriarte, E., Santana, L., Rabadan, R., Friedman, C.: Drug\u2013drug interaction through molecular structure similarity analysis. J. Am. Med. Inform. Assoc. 19(6), 1066\u20131074 (2012)","journal-title":"J. Am. Med. Inform. Assoc."},{"issue":"1","key":"30_CR4","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1186\/s12859-016-1415-9","volume":"18","author":"W Zhang","year":"2017","unstructured":"Zhang, W., Chen, Y., Liu, F., Luo, F., Tian, G., Li, X.: Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data. BMC Bioinformatics. 18(1), 18 (2017)","journal-title":"BMC Bioinformatics"},{"key":"30_CR5","unstructured":"BioSNAP Datasets: Stanford Biomedical Network Dataset Collection, http:\/\/snap.stanford.edu\/biodata. Accessed 26 March 2026."},{"issue":"18","key":"30_CR6","doi-asserted-by":"publisher","first-page":"E4304","DOI":"10.1073\/pnas.1803294115","volume":"115","author":"JY Ryu","year":"2018","unstructured":"Ryu, J.Y., Kim, H.U., Lee, S.Y.: Deep learning improves prediction of drug\u2013drug and drug\u2013food interactions. Proc. Natl. Acad. Sci. 115(18), E4304\u2013E4311 (2018)","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"D1","key":"30_CR7","doi-asserted-by":"publisher","first-page":"D1074","DOI":"10.1093\/nar\/gkx1037","volume":"46","author":"DS Wishart","year":"2018","unstructured":"Wishart, D.S., et al.: DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46(D1), D1074\u2013D1082 (2018)","journal-title":"Nucleic Acids Res."},{"key":"30_CR8","first-page":"2921","volume":"2021","author":"Y Wang","year":"2021","unstructured":"Wang, Y., Min, Y., Chen, X., Wu, J.: Multi-view graph contrastive representation learning for drug-drug interaction prediction. Proc. Web Conf. 2021, 2921\u20132933 (2021)","journal-title":"Proc. Web Conf."},{"issue":"1","key":"30_CR9","doi-asserted-by":"publisher","first-page":"12339","DOI":"10.1038\/srep12339","volume":"5","author":"P Zhang","year":"2015","unstructured":"Zhang, P., Wang, F., Hu, J., Sorrentino, R.: Label propagation prediction of drug-drug interactions based on clinical side effects. Sci. Rep. 5(1), 12339 (2015)","journal-title":"Sci. Rep."},{"key":"30_CR10","unstructured":"Chithrananda, S., Grand, G., Ramsundar, B.: ChemBERTa: large-scale self-supervised pretraining for molecular property prediction. arXiv Preprint https:\/\/arxiv.org\/abs\/2010.09885 (2020)"},{"key":"30_CR11","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. Adv. Neural Inf. Proces. Syst. 30 (2017)"},{"key":"30_CR12","doi-asserted-by":"crossref","unstructured":"Ma, T., Xiao, C., Zhou, J., Wang, F.: Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders. In: Lang, J. (ed.) Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018, pp. 3477\u20133483. ijcai.org, Stockholm (2018).","DOI":"10.24963\/ijcai.2018\/483"},{"key":"30_CR13","unstructured":"Kipf, T.N., Welling, M.: Semi-Supervised Classification with Graph Convolutional Networks. In: 5th International Conference on Learning Representations, ICLR 2017, Conference Track Proceedings, Toulon, France (2017)"},{"key":"30_CR14","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA (2019)"},{"key":"30_CR15","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks. In: 6th International Conference on Learning Representations, ICLR 2018, Conference Track Proceedings, Vancouver, BC, Canada (2018)"},{"key":"30_CR16","doi-asserted-by":"crossref","unstructured":"Li, J., Rong, Y., Cheng, H., Meng, H., Huang, W., Huang, J.: Semi-supervised graph classification: a hierarchical graph perspective. In: The World Wide Web Conference, pp. 972\u2013982 (2019).","DOI":"10.1145\/3308558.3313461"},{"key":"30_CR17","unstructured":"Duvenaud, D.K., et al.: Convolutional networks on graphs for learning molecular fingerprints. In: Advances in Neural Information Processing Systems, pp. 2224\u20132232 (2015)"},{"key":"30_CR18","doi-asserted-by":"crossref","unstructured":"Lv, G., Hu, Z., Bi, Y., Zhang, S.: Learning unknown from correlations: graph neural network for inter-novel-protein interaction prediction. In: Zhou, Z.H. (ed.) Proceedings of the 30th International Joint Conference on Artificial Intelligence, IJCAI-21, Main Track, pp. 3677\u20133683. ijcai.org, Montreal (2021).","DOI":"10.24963\/ijcai.2021\/506"}],"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\/978-981-92-3403-5_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T20:07:17Z","timestamp":1784405237000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-3403-5_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,19]]},"ISBN":["9789819234028","9789819234035"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-3403-5_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,19]]},"assertion":[{"value":"19 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"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"}}]}}