{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T05:51:32Z","timestamp":1780465892439,"version":"3.54.1"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"S7","license":[{"start":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T00:00:00Z","timestamp":1660435200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T00:00:00Z","timestamp":1660435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Extraction of drug drug interactions from biomedical literature and other textual data is an important component to monitor drug-safety and this has attracted attention of many researchers in healthcare. Existing works are more pivoted around relation extraction using bidirectional long short-term memory networks (BiLSTM) and BERT model which does not attain the best feature representations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Our proposed DDI (drug drug interaction) prediction model provides multiple advantages: (1) The newly proposed attention vector is added to better deal with the problem of overlapping relations, (2) The molecular structure information of drugs is integrated into the model to better express the functional group structure of drugs, (3) We also added text features that combined the T-distribution and chi-square distribution to make the model more focused on drug entities and (4) it achieves similar or better prediction performance (F-scores up to 85.16%) compared to state-of-the-art DDI models when tested on benchmark datasets.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our model that leverages state of the art transformer architecture in conjunction with multiple features can bolster the performances of drug drug interation tasks in the biomedical domain. In particular, we believe our research would be helpful in identification of potential adverse drug reactions.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12859-022-04876-8","type":"journal-article","created":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T05:02:44Z","timestamp":1660453364000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["IMSE: interaction information attention and molecular structure based drug drug interaction extraction"],"prefix":"10.1186","volume":"23","author":[{"given":"Biao","family":"Duan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"issue":"17","key":"4876_CR1","doi-asserted-by":"publisher","first-page":"1818","DOI":"10.1001\/jama.2015.13766","volume":"314","author":"ED Kantor","year":"2015","unstructured":"Kantor ED, Rehm CD, Haas JS, Chan AT, Giovannucci EL. Trends in prescription drug use among adults in the United States from 1999\u20132012. JAMA. 2015;314(17):1818\u201330.","journal-title":"JAMA"},{"key":"4876_CR2","doi-asserted-by":"publisher","first-page":"326","DOI":"10.3389\/fphar.2020.00326","volume":"11","author":"N Zhang","year":"2020","unstructured":"Zhang N, Sundquist J, Sundquist K, Ji J. An increasing trend in the prevalence of polypharmacy in Sweden: a nationwide register-based study. Front Pharmacol. 2020;11:326.","journal-title":"Front Pharmacol"},{"issue":"3","key":"4876_CR3","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0214240","volume":"14","author":"MP Oktora","year":"2019","unstructured":"Oktora MP, Denig P, Bos JH, Schuiling-Veninga CC, Hak E. Trends in polypharmacy and dispensed drugs among adults in the Netherlands as compared to the United States. PLoS ONE. 2019;14(3): e0214240.","journal-title":"PLoS ONE"},{"issue":"6","key":"4876_CR4","doi-asserted-by":"publisher","first-page":"e33","DOI":"10.1345\/aph.1Q013","volume":"45","author":"A Siniscalchi","year":"2011","unstructured":"Siniscalchi A, Gallelli L, Avenoso T, Squillace A, De Sarro G. Effects of carbamazepine\/oxycodone coadministration in the treatment of trigeminal neuralgia. Ann Pharmacother. 2011;45(6):e33\u2013e33.","journal-title":"Ann Pharmacother"},{"issue":"1","key":"4876_CR5","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.phrs.2003.08.001","volume":"49","author":"A Franceschi","year":"2004","unstructured":"Franceschi A, Tuccori M, Bocci G, Vannozzi F, Di Paolo A, Barbara C, et al. Drug therapeutic failures in emergency department patients: a university hospital experience. Pharmacol Res. 2004;49(1):85\u201391.","journal-title":"Pharmacol Res"},{"key":"4876_CR6","volume-title":"Semeval-2013 task 9: extraction of drug\u2013drug interactions from biomedical texts (DDIExtraction 2013)","author":"I Segura Bedmar","year":"2013","unstructured":"Segura Bedmar I, Mart\u00ednez P, Herrero Zazo M. Semeval-2013 task 9: extraction of drug\u2013drug interactions from biomedical texts (DDIExtraction 2013). Stroudsburg: Association for Computational Linguistics; 2013."},{"key":"4876_CR7","volume-title":"Application of information extraction techniques to pharmacological domain: extracting drug\u2013drug interactions","author":"Bedmar I Segura","year":"2010","unstructured":"Segura Bedmar I. Application of information extraction techniques to pharmacological domain: extracting drug\u2013drug interactions. Madrid: Universidad Carlos III de Madrid; 2010."},{"key":"4876_CR8","unstructured":"Garc\u00eda-Blasco S, Danger Mercaderes R, Rosso P. Drug-drug interaction detection: a new approach based on maximal frequent sequences [J]. 2010."},{"key":"4876_CR9","first-page":"6918381","volume":"2016","author":"S Liu","year":"2016","unstructured":"Liu S, Tang B, Chen Q, Wang X. Drug\u2013drug interaction extraction via convolutional neural networks. Comput Math Methods Med. 2016;2016:6918381.","journal-title":"Comput Math Methods Med"},{"key":"4876_CR10","first-page":"1850404","volume":"2016","author":"C Quan","year":"2016","unstructured":"Quan C, Hua L, Sun X, Bai W. Multichannel convolutional neural network for biological relation extraction. BioMed Res Int. 2016;2016:1850404.","journal-title":"BioMed Res Int"},{"key":"4876_CR11","doi-asserted-by":"crossref","unstructured":"Liu S, Chen K, Chen Q, Tang B. Dependency-based convolutional neural network for drug\u2013drug interaction extraction. In: IEEE International conference on bioinformatics and biomedicine (BIBM), vol 2016. IEEE; 2016. p. 1074\u201380.","DOI":"10.1109\/BIBM.2016.7822671"},{"issue":"22","key":"4876_CR12","doi-asserted-by":"crossref","first-page":"3444","DOI":"10.1093\/bioinformatics\/btw486","volume":"32","author":"Z Zhao","year":"2016","unstructured":"Zhao Z, Yang Z, Luo L, Lin H, Wang J. Drug drug interaction extraction from biomedical literature using syntax convolutional neural network. Bioinformatics. 2016;32(22):3444\u201353.","journal-title":"Bioinformatics"},{"key":"4876_CR13","doi-asserted-by":"crossref","unstructured":"Dewi IN, Dong S, Hu J. Drug\u2013drug interaction relation extraction with deep convolutional neural networks. In: 2017 IEEE International conference on bioinformatics and biomedicine (BIBM). IEEE; 2017. p. 1795\u2013802.","DOI":"10.1109\/BIBM.2017.8217933"},{"key":"4876_CR14","doi-asserted-by":"crossref","unstructured":"Sun X, Ma L, Du X, Feng J, Dong K. Deep convolution neural networks for drug\u2013drug interaction extraction. In: 2018 IEEE International conference on bioinformatics and biomedicine (BIBM), vol 2018. IEEE; 2018. p. 1662\u20138.","DOI":"10.1109\/BIBM.2018.8621405"},{"key":"4876_CR15","doi-asserted-by":"crossref","unstructured":"Asada M, Miwa M, Sasaki Y. Extracting drug\u2013drug interactions with attention CNNs. In: BioNLP 2017; 2017. p. 9\u201318.","DOI":"10.18653\/v1\/W17-2302"},{"key":"4876_CR16","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.ins.2017.06.021","volume":"415","author":"D Huang","year":"2017","unstructured":"Huang D, Jiang Z, Zou L, Li L. Drug\u2013drug interaction extraction from biomedical literature using support vector machine and long short term memory networks. Inf Sci. 2017;415:100\u20139.","journal-title":"Inf Sci"},{"key":"4876_CR17","doi-asserted-by":"crossref","unstructured":"Jiang Z, Gu L, Jiang Q. Drug drug interaction extraction from literature using a skeleton long short term memory neural network. In: 2017 IEEE International conference on bioinformatics and biomedicine (BIBM). IEEE; 2017. p. 552\u20135.","DOI":"10.1109\/BIBM.2017.8217708"},{"issue":"16","key":"4876_CR18","first-page":"99","volume":"18","author":"W Wang","year":"2017","unstructured":"Wang W, Yang X, Yang C, Guo X, Zhang X, Wu C. Dependency-based long short term memory network for drug\u2013drug interaction extraction. BMC Bioinform. 2017;18(16):99\u2013109.","journal-title":"BMC Bioinform"},{"issue":"1","key":"4876_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-017-1855-x","volume":"18","author":"W Zheng","year":"2017","unstructured":"Zheng W, Lin H, Luo L, Zhao Z, Li Z, Zhang Y, et al. An attention-based effective neural model for drug\u2013drug interactions extraction. BMC Bioinform. 2017;18(1):1\u201311.","journal-title":"BMC Bioinform"},{"issue":"5","key":"4876_CR20","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1093\/bioinformatics\/btx659","volume":"34","author":"Y Zhang","year":"2018","unstructured":"Zhang Y, Zheng W, Lin H, Wang J, Yang Z, Dumontier M. Drug\u2013drug interaction extraction via hierarchical RNNs on sequence and shortest dependency paths. Bioinformatics. 2018;34(5):828\u201335.","journal-title":"Bioinformatics"},{"key":"4876_CR21","doi-asserted-by":"crossref","unstructured":"Yi Z, Li S, Yu J, Tan Y, Wu Q, Yuan H, et\u00a0al. Drug\u2013drug interaction extraction via recurrent neural network with multiple attention layers. In: International conference on advanced data mining and applications. Springer; 2017. p. 554\u201366.","DOI":"10.1007\/978-3-319-69179-4_39"},{"key":"4876_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.artmed.2018.03.001","volume":"87","author":"D Zhou","year":"2018","unstructured":"Zhou D, Miao L, He Y. Position-aware deep multi-task learning for drug\u2013drug interaction extraction. Artif Intell Med. 2018;87:1\u20138.","journal-title":"Artif Intell Med"},{"issue":"ARTICLE","key":"4876_CR23","first-page":"2493","volume":"12","author":"R Collobert","year":"2011","unstructured":"Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa P. Natural language processing (almost) from scratch. J Mach Learn Res. 2011;12(ARTICLE):2493\u2013537.","journal-title":"J Mach Learn Res"},{"key":"4876_CR24","unstructured":"Sutskever I, Vinyals O, Le QV. Sequence to sequence learning with neural networks. In: Advances in neural information processing systems; 2014. p. 3104\u201312."},{"key":"4876_CR25","unstructured":"Pascanu R, Mikolov T, Bengio Y. On the difficulty of training recurrent neural networks. In: International conference on machine learning. PMLR; 2013. p. 1310\u20138."},{"issue":"8","key":"4876_CR26","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735\u201380.","journal-title":"Neural Comput"},{"issue":"10","key":"4876_CR27","doi-asserted-by":"publisher","first-page":"2451","DOI":"10.1162\/089976600300015015","volume":"12","author":"FA Gers","year":"2000","unstructured":"Gers FA, Schmidhuber J, Cummins F. Learning to forget: continual prediction with LSTM. Neural Comput. 2000;12(10):2451\u201371.","journal-title":"Neural Comput"},{"key":"4876_CR28","doi-asserted-by":"crossref","unstructured":"Cho K, Van\u00a0Merri\u00ebnboer B, Bahdanau D, Bengio Y. On the properties of neural machine translation: encoder\u2013decoder approaches. arXiv preprint arXiv:1409.1259 (2014).","DOI":"10.3115\/v1\/W14-4012"},{"key":"4876_CR29","unstructured":"Devlin J, Chang MW, Lee K, Toutanova K. Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)."},{"key":"4876_CR30","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et\u00a0al. Attention is all you need. arXiv preprint arXiv:1706.03762 (2017)."},{"issue":"4","key":"4876_CR31","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","volume":"36","author":"J Lee","year":"2020","unstructured":"Lee J, Yoon W, Kim S, Kim D, Kim S, So CH, et al. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics. 2020;36(4):1234\u201340.","journal-title":"Bioinformatics"},{"key":"4876_CR32","doi-asserted-by":"crossref","unstructured":"Boukkouri HE, Ferret O, Lavergne T, Noji H, Zweigenbaum P, Tsujii J. CharacterBERT: reconciling ELMo and BERT for word-level open-vocabulary representations from characters. arXiv preprint arXiv:2010.10392 (2020).","DOI":"10.18653\/v1\/2020.coling-main.609"},{"issue":"15","key":"4876_CR33","doi-asserted-by":"publisher","first-page":"4323","DOI":"10.1093\/bioinformatics\/btaa491","volume":"36","author":"C Sun","year":"2020","unstructured":"Sun C, Yang Z, Su L, Wang L, Zhang Y, Lin H, et al. Chemical\u2013protein interaction extraction via Gaussian probability distribution and external biomedical knowledge. Bioinformatics. 2020;36(15):4323\u201330.","journal-title":"Bioinformatics"},{"issue":"D1","key":"4876_CR34","doi-asserted-by":"publisher","first-page":"D1074","DOI":"10.1093\/nar\/gkx1037","volume":"46","author":"DS Wishart","year":"2018","unstructured":"Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018;46(D1):D1074\u201382.","journal-title":"Nucleic Acids Res"},{"key":"4876_CR35","unstructured":"Landrum. RDKit: open-source cheminformatics. Release 2014.03.1. 2010."},{"issue":"2","key":"4876_CR36","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\u2013protein interaction prediction with end-to-end learning of neural networks for graphs and sequences. Bioinformatics. 2019;35(2):309\u201318.","journal-title":"Bioinformatics"},{"issue":"1","key":"4876_CR37","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0190926","volume":"13","author":"S Lim","year":"2018","unstructured":"Lim S, Lee K, Kang J. Drug drug interaction extraction from the literature using a recursive neural network. PLoS ONE. 2018;13(1): e0190926.","journal-title":"PLoS ONE"},{"key":"4876_CR38","doi-asserted-by":"crossref","unstructured":"Qin L, Dong G, Peng J. Chemical\u2013protein interaction extraction via chemicalBERT and attention guided graph convolutional networks in parallel. In: 2020 IEEE International conference on bioinformatics and biomedicine (BIBM). IEEE; 2020. p. 708\u201315.","DOI":"10.1109\/BIBM49941.2020.9313234"},{"issue":"12","key":"4876_CR39","doi-asserted-by":"publisher","first-page":"1739","DOI":"10.1093\/bioinformatics\/btaa907","volume":"37","author":"M Asada","year":"2021","unstructured":"Asada M, Miwa M, Sasaki Y. Using drug descriptions and molecular structures for drug\u2013drug interaction extraction from literature. Bioinformatics. 2021;37(12):1739\u201346.","journal-title":"Bioinformatics"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-022-04876-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-022-04876-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-022-04876-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T21:42:46Z","timestamp":1676410966000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-022-04876-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,14]]},"references-count":39,"journal-issue":{"issue":"S7","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["4876"],"URL":"https:\/\/doi.org\/10.1186\/s12859-022-04876-8","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,14]]},"assertion":[{"value":"28 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 August 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 August 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"338"}}