{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T11:46:39Z","timestamp":1742989599399,"version":"3.40.3"},"publisher-location":"Cham","reference-count":10,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030304928"},{"type":"electronic","value":"9783030304935"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The discovery of potential Drug-Target Interactions (DTIs) is a determining step in the drug discovery and repositioning process, as the effectiveness of the currently available antibiotic treatment is declining. Successful approaches have been presented to solve this problem but seldom protein sequences and structured data are used together. We present a deep learning architecture model, which exploits the particular ability of Convolutional Neural Networks (CNNs) to obtain 1D representations from protein amino acid sequences and SMILES (Simplified Molecular Input Line Entry System) strings. The results achieved demonstrate that using CNNs to obtain representations of the data, instead of the traditional descriptors, lead to improved performance.<\/jats:p>","DOI":"10.1007\/978-3-030-30493-5_76","type":"book-chapter","created":{"date-parts":[[2019,9,10]],"date-time":"2019-09-10T20:03:41Z","timestamp":1568145821000},"page":"804-809","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep Neural Network Architecture for Drug-Target Interaction Prediction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2147-7321","authenticated-orcid":false,"given":"Nelson R. C.","family":"Monteiro","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9770-7672","authenticated-orcid":false,"given":"Bernardete","family":"Ribeiro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4937-2334","authenticated-orcid":false,"given":"Joel P.","family":"Arrais","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,9,9]]},"reference":[{"key":"76_CR1","doi-asserted-by":"publisher","first-page":"1645","DOI":"10.2147\/IDR.S173867","volume":"11","author":"B Aslam","year":"2018","unstructured":"Aslam, B., et al.: Antibiotic resistance: a rundown of a global crisis. Infect. Drug Resist. 11, 1645\u20131658 (2018). https:\/\/doi.org\/10.2147\/IDR.S173867. 30349322[pmid]","journal-title":"Infect. Drug Resist."},{"issue":"11","key":"76_CR2","doi-asserted-by":"publisher","first-page":"3086","DOI":"10.1021\/ci400127q","volume":"53","author":"DS Cao","year":"2013","unstructured":"Cao, D.S., Liang, Y.Z., Yan, J., Tan, G.S., Xu, Q.S., Liu, S.: PyDPI: freely available python package for chemoinformatics, bioinformatics, and chemogenomics studies. J. Chem. Inf. Model. 53(11), 3086\u20133096 (2013). https:\/\/doi.org\/10.1021\/ci400127q","journal-title":"J. Chem. Inf. Model."},{"issue":"10","key":"76_CR3","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1002\/minf.201400009","volume":"33","author":"DS Cao","year":"2014","unstructured":"Cao, D.S., et al.: Computational prediction of drug target interactions using chemical, biological, and network features. Mol. Inform. 33(10), 669\u2013681 (2014). https:\/\/doi.org\/10.1002\/minf.201400009","journal-title":"Mol. Inform."},{"issue":"9","key":"76_CR4","doi-asserted-by":"publisher","first-page":"2208","DOI":"10.3390\/molecules23092208","volume":"23","author":"R Chen","year":"2018","unstructured":"Chen, R., Liu, X., Jin, S., Lin, J., Liu, J.: Machine learning for drug-target interaction prediction. Molecules 23(9), 2208 (2018). https:\/\/doi.org\/10.3390\/molecules23092208","journal-title":"Molecules"},{"issue":"1","key":"76_CR5","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1038\/nbt1273","volume":"25","author":"AC Cheng","year":"2007","unstructured":"Cheng, A.C., et al.: Structure-based maximal affinity model predicts small-molecule druggability. Nat. Biotechnol. 25(1), 71 (2007). https:\/\/doi.org\/10.1038\/nbt1273","journal-title":"Nat. Biotechnol."},{"issue":"9","key":"76_CR6","doi-asserted-by":"publisher","first-page":"2373","DOI":"10.1039\/C2MB25110H","volume":"8","author":"F Cheng","year":"2012","unstructured":"Cheng, F., Zhou, Y., Li, J., Li, W., Liu, G., Tang, Y.: Prediction of chemical-protein interactions: multitarget-QSAR versus computational chemogenomic methods. Mol. BioSyst. 8(9), 2373\u20132384 (2012). https:\/\/doi.org\/10.1039\/C2MB25110H","journal-title":"Mol. BioSyst."},{"issue":"11","key":"76_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pcbi.1005219","volume":"12","author":"ED Coelho","year":"2016","unstructured":"Coelho, E.D., Arrais, J.P., Oliveira, J.L.: Computational discovery of putative leads for drug repositioning through drug-target interaction prediction. PLOS Comput. Biol. 12(11), 1\u201317 (2016). https:\/\/doi.org\/10.1371\/journal.pcbi.1005219. 11","journal-title":"PLOS Comput. Biol."},{"issue":"13","key":"76_CR8","doi-asserted-by":"publisher","first-page":"i232","DOI":"10.1093\/bioinformatics\/btn162","volume":"24","author":"A Gutteridge","year":"2008","unstructured":"Gutteridge, A., Araki, M., Kanehisa, M., Honda, W., Yamanishi, Y.: Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics 24(13), i232\u2013i240 (2008). https:\/\/doi.org\/10.1093\/bioinformatics\/btn162","journal-title":"Bioinformatics"},{"key":"76_CR9","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.ymeth.2016.06.024","volume":"110","author":"K Tian","year":"2016","unstructured":"Tian, K., Shao, M., Wang, Y., Guan, J., Zhou, S.: Boosting compound-protein interaction prediction by deep learning. Methods 110, 64\u201372 (2016). https:\/\/doi.org\/10.1016\/j.ymeth.2016.06.024","journal-title":"Methods"},{"issue":"1","key":"76_CR10","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1186\/1471-2105-11-167","volume":"11","author":"CY Yu","year":"2010","unstructured":"Yu, C.Y., Chou, L.C., Chang, D.T.H.: Predicting protein-protein interactions in unbalanced data using the primary structure of proteins. BMC Bioinform. 11(1), 167 (2010). https:\/\/doi.org\/10.1186\/1471-2105-11-167","journal-title":"BMC Bioinform."}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2019: Workshop and Special Sessions"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-30493-5_76","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T16:55:42Z","timestamp":1710348942000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-30493-5_76"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030304928","9783030304935"],"references-count":10,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-30493-5_76","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"9 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Munich","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}