{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:43:19Z","timestamp":1772120599729,"version":"3.50.1"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031672774","type":"print"},{"value":"9783031672781","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-67278-1_2","type":"book-chapter","created":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T06:02:45Z","timestamp":1723528965000},"page":"17-30","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["GraphDDI: Graph Neural Network for\u00a0Prediction of\u00a0Drug-Drug Interaction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9995-1858","authenticated-orcid":false,"given":"Suyash","family":"Gupta","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1269-3778","authenticated-orcid":false,"given":"Siddhartha","family":"Laghuvarapu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7114-3955","authenticated-orcid":false,"given":"U. Deva","family":"Priyakumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,14]]},"reference":[{"key":"2_CR1","unstructured":"Buterez, D., Janet, J.P., Kiddle, S.J., Oglic, D., Li\u00f2, P.: Graph neural networks with adaptive readouts. In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (eds.) Advances in Neural Information Processing Systems, vol.\u00a035, pp. 19746\u201319758. Curran Associates, Inc. (2022)"},{"issue":"17","key":"2_CR2","doi-asserted-by":"publisher","first-page":"2651","DOI":"10.1093\/bioinformatics\/btab169","volume":"37","author":"Y Chen","year":"2021","unstructured":"Chen, Y., Ma, T., Yang, X., Wang, J., Song, B., Zeng, X.: MUFFIN: multi-scale feature fusion for drug-drug interaction prediction. Bioinformatics 37(17), 2651\u20132658 (2021)","journal-title":"Bioinformatics"},{"key":"2_CR3","unstructured":"Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)"},{"key":"2_CR4","first-page":"13260","volume":"33","author":"G Corso","year":"2020","unstructured":"Corso, G., Cavalleri, L., Beaini, D., Li\u00f2, P., Veli\u010dkovi\u0107, P.: Principal neighbourhood aggregation for graph nets. Adv. Neural. Inf. Process. Syst. 33, 13260\u201313271 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"15","key":"2_CR5","doi-asserted-by":"publisher","first-page":"4316","DOI":"10.1093\/bioinformatics\/btaa501","volume":"36","author":"Y Deng","year":"2020","unstructured":"Deng, Y., Xu, X., Qiu, Y., Xia, J., Zhang, W., Liu, S.: A multimodal deep learning framework for predicting drug-drug interaction events. Bioinformatics 36(15), 4316\u20134322 (2020)","journal-title":"Bioinformatics"},{"key":"2_CR6","unstructured":"Drug Interaction Checker. https:\/\/go.drugbank.com\/drug-interaction-checker. Accessed 11 Apr 2024"},{"key":"2_CR7","unstructured":"Drug Interaction Checker. https:\/\/www.drugs.com\/drug_interactions.html. Accessed 11 Apr 2024"},{"issue":"3","key":"2_CR8","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1111\/bcpt.12527","volume":"118","author":"ZN Ennis","year":"2016","unstructured":"Ennis, Z.N., Dideriksen, D., V\u00e6gter, H.B., Handberg, G., Potteg\u00e5rd, A.: Acetaminophen for chronic pain: a systematic review on efficacy. Basic Clin. Pharmacol. Toxicol. 118(3), 184\u2013189 (2016)","journal-title":"Basic Clin. Pharmacol. Toxicol."},{"key":"2_CR9","unstructured":"Fey, M., Lenssen, J.E.: Fast graph representation learning with PyTorch Geometric. In: ICLR Workshop on Representation Learning on Graphs and Manifolds (2019)"},{"key":"2_CR10","unstructured":"Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: International Conference on Machine Learning, pp. 1263\u20131272. PMLR (2017)"},{"key":"2_CR11","unstructured":"Huang, K., et al.: Therapeutics data commons: machine learning datasets and tasks for drug discovery and development. In: Proceedings of Neural Information Processing Systems, NeurIPS Datasets and Benchmarks (2021)"},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Huang, K., Xiao, C., Hoang, T., Glass, L., Sun, J.: CASTER: predicting drug interactions with chemical substructure representation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a034, pp. 702\u2013709 (2020)","DOI":"10.1609\/aaai.v34i01.5412"},{"key":"2_CR13","doi-asserted-by":"publisher","first-page":"911","DOI":"10.2165\/00002018-200730100-00009","volume":"30","author":"K Johnell","year":"2007","unstructured":"Johnell, K., Klarin, I.: The relationship between number of drugs and potential drug-drug interactions in the elderly. Drug Saf. 30, 911\u2013918 (2007)","journal-title":"Drug Saf."},{"key":"2_CR14","unstructured":"Landrum, G., et\u00a0al.: RDKit: Open-source cheminformatics (2006). http:\/\/www.rdkit.org"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Nyamabo, A.K., Yu, H., Shi, J.Y.: SSI\u2013DDI: substructure\u2013substructure interactions for drug\u2013drug interaction prediction. Brief. Bioinform. 22(6), bbab133 (2021)","DOI":"10.1093\/bib\/bbab133"},{"issue":"7","key":"2_CR16","first-page":"601","volume":"18","author":"C Palleria","year":"2013","unstructured":"Palleria, C., et al.: Pharmacokinetic drug-drug interaction and their implication in clinical management. J. Res. Med. Sci. Official J. Isfahan Univ. Med. Sci. 18(7), 601 (2013)","journal-title":"J. Res. Med. Sci. Official J. Isfahan Univ. Med. Sci."},{"issue":"18","key":"2_CR17","doi-asserted-by":"publisher","first-page":"4387","DOI":"10.1093\/bioinformatics\/btac538","volume":"38","author":"S Ren","year":"2022","unstructured":"Ren, S., Yu, L., Gao, L.: Multidrug representation learning based on pretraining model and molecular graph for drug interaction and combination prediction. Bioinformatics 38(18), 4387\u20134394 (2022)","journal-title":"Bioinformatics"},{"key":"2_CR18","doi-asserted-by":"crossref","unstructured":"Riechelmann, R., Girardi, D.: Drug interactions in cancer patients: a hidden risk? (2016)","DOI":"10.4103\/2279-042X.179560"},{"issue":"18","key":"2_CR19","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-drug and drug-food interactions. Proc. Natl. Acad. Sci. 115(18), E4304\u2013E4311 (2018)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"2_CR20","unstructured":"Velickovic, P., et al.: Graph attention networks. Stat 1050(20), 10\u201348550 (2017)"},{"key":"2_CR21","unstructured":"Vinyals, O., Bengio, S., Kudlur, M.: Order matters: sequence to sequence for sets. arXiv preprint arXiv:1511.06391 (2015)"},{"key":"2_CR22","doi-asserted-by":"crossref","unstructured":"Wang, C.S., Lin, P.J., Cheng, C.L., Tai, S.H., Kao Yang, Y.H., Chiang, J.H.: Detecting potential adverse drug reactions using a deep neural network model. J. Med. Internet Res. 21(2), e11016 (2019)","DOI":"10.2196\/11016"},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"Wang, H., Lian, D., Zhang, Y., Qin, L., Lin, X.: GoGNN: graph of graphs neural network for predicting structured entity interactions. In: Bessiere, C. (ed.) Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-2020, pp. 1317\u20131323. International Joint Conferences on Artificial Intelligence Organization (2020)","DOI":"10.24963\/ijcai.2020\/183"},{"key":"2_CR24","doi-asserted-by":"crossref","unstructured":"Wishart, D.S., et\u00a0al.: DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46(D1), D1074\u2013D1082 (2018)","DOI":"10.1093\/nar\/gkx1037"},{"key":"2_CR25","doi-asserted-by":"crossref","unstructured":"Xiong, Z., et al.: Multi-relational contrastive learning graph neural network for drug-drug interaction event prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a037, pp. 5339\u20135347 (2023)","DOI":"10.1609\/aaai.v37i4.25665"},{"issue":"8","key":"2_CR26","doi-asserted-by":"publisher","first-page":"3370","DOI":"10.1021\/acs.jcim.9b00237","volume":"59","author":"K Yang","year":"2019","unstructured":"Yang, K., et al.: Analyzing learned molecular representations for property prediction. J. Chem. Inf. Model. 59(8), 3370\u20133388 (2019)","journal-title":"J. Chem. Inf. Model."},{"issue":"18","key":"2_CR27","doi-asserted-by":"publisher","first-page":"2988","DOI":"10.1093\/bioinformatics\/btab207","volume":"37","author":"Y Yu","year":"2021","unstructured":"Yu, Y., Huang, K., Zhang, C., Glass, L.M., Sun, J., Xiao, C.: SumGNN: multi-typed drug interaction prediction via efficient knowledge graph summarization. Bioinformatics 37(18), 2988\u20132995 (2021)","journal-title":"Bioinformatics"},{"key":"2_CR28","unstructured":"Zitnik, M., Sosi\u010d, R., Maheshwari, S., Leskovec, J.: BioSNAP Datasets: Stanford biomedical network dataset collection (2018). http:\/\/snap.stanford.edu\/biodata"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Healthcare"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-67278-1_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T06:03:08Z","timestamp":1723528988000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-67278-1_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031672774","9783031672781"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-67278-1_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"14 August 2024","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":"AIiH","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on AI in Healthcare","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Swansea","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aiih2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aiih.cc","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}