{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T17:56:53Z","timestamp":1777485413639,"version":"3.51.4"},"reference-count":154,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,4,14]],"date-time":"2024-04-14T00:00:00Z","timestamp":1713052800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,14]],"date-time":"2024-04-14T00:00:00Z","timestamp":1713052800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"jkstic dst"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s00530-024-01325-9","type":"journal-article","created":{"date-parts":[[2024,4,14]],"date-time":"2024-04-14T11:01:40Z","timestamp":1713092500000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A review of deep learning algorithms for modeling drug interactions"],"prefix":"10.1007","volume":"30","author":[{"given":"Aga Basit","family":"Iqbal","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Idris Afzal","family":"Shah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Injila","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Assif","family":"Assad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mushtaq","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Syed Zubair","family":"Shah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,14]]},"reference":[{"issue":"1","key":"1325_CR1","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1002\/minf.201501008","volume":"35","author":"E Gawehn","year":"2016","unstructured":"Gawehn, E., Hiss, J.A., Schneider, G.: Deep learning in drug discovery. Mol. Inform. 35(1), 3\u201314 (2016). https:\/\/doi.org\/10.1002\/minf.201501008","journal-title":"Mol. Inform."},{"issue":"4","key":"1325_CR2","doi-asserted-by":"publisher","first-page":"1401","DOI":"10.1021\/acs.jproteome.6b00618","volume":"16","author":"M Wen","year":"2017","unstructured":"Wen, M., et al.: Deep-learning-based drug-target interaction prediction. J. Proteome Res. 16(4), 1401\u20131409 (2017). https:\/\/doi.org\/10.1021\/acs.jproteome.6b00618","journal-title":"J. Proteome Res."},{"issue":"4","key":"1325_CR3","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1049\/iet-syb.2019.0116","volume":"14","author":"PK Shukla","year":"2020","unstructured":"Shukla, P.K., et al.: Efficient prediction of drug\u2013drug interaction using deep learning models. IET Syst. Biol. 14(4), 211\u2013216 (2020). https:\/\/doi.org\/10.1049\/iet-syb.2019.0116","journal-title":"IET Syst. Biol."},{"key":"1325_CR4","doi-asserted-by":"publisher","unstructured":"K. Preuer, G. Klambauer, F. Rippmann, S. Hochreiter, T. Unterthiner, Interpretable deep learning in drug discovery. In: Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11700 LNCS, pp. 331\u2013345 (2019). https:\/\/doi.org\/10.1007\/978-3-030-28954-6_18.","DOI":"10.1007\/978-3-030-28954-6_18"},{"issue":"6","key":"1325_CR5","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.1016\/j.drudis.2018.01.039","volume":"23","author":"H Chen","year":"2018","unstructured":"Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., Blaschke, T.: The rise of deep learning in drug discovery. Drug Discov. TodayDiscov. Today 23(6), 1241\u20131250 (2018). https:\/\/doi.org\/10.1016\/j.drudis.2018.01.039","journal-title":"Drug Discov. TodayDiscov. Today"},{"issue":"10","key":"1325_CR6","doi-asserted-by":"publisher","first-page":"2017","DOI":"10.1016\/j.drudis.2019.07.006","volume":"24","author":"A Lavecchia","year":"2019","unstructured":"Lavecchia, A.: Deep learning in drug discovery: opportunities, challenges and future prospects. Drug Discov. TodayDiscov. Today 24(10), 2017\u20132032 (2019). https:\/\/doi.org\/10.1016\/j.drudis.2019.07.006","journal-title":"Drug Discov. TodayDiscov. Today"},{"key":"1325_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-022-10306-1","volume-title":"Deep learning in drug discovery: an integrative review and future challenges, no. 0123456789","author":"H Askr","year":"2022","unstructured":"Askr, H., Elgeldawi, E., Aboul Ella, H., Elshaier, Y.A.M.M., Gomaa, M.M., Hassanien, A.E.: Deep learning in drug discovery: an integrative review and future challenges, no. 0123456789. Springer, Netherlands (2022). https:\/\/doi.org\/10.1007\/s10462-022-10306-1"},{"key":"1325_CR8","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12123067","author":"PN Srinivasu","year":"2022","unstructured":"Srinivasu, P.N., Shafi, J., Krishna, T.B., Sujatha, C.N., Praveen, S.P., Ijaz, M.F.: Using recurrent neural networks for predicting type-2 diabetes from genomic and tabular data. Diagnostics (2022). https:\/\/doi.org\/10.3390\/diagnostics12123067","journal-title":"Diagnostics"},{"issue":"1","key":"1325_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-25089-2","volume":"12","author":"SP Praveen","year":"2022","unstructured":"Praveen, S.P., Srinivasu, P.N., Shafi, J., Wozniak, M., Ijaz, M.F.: ResNet-32 and FastAI for diagnoses of ductal carcinoma from 2D tissue slides. Sci. Rep. 12(1), 1\u201316 (2022). https:\/\/doi.org\/10.1038\/s41598-022-25089-2","journal-title":"Sci. Rep."},{"key":"1325_CR10","doi-asserted-by":"publisher","DOI":"10.3390\/bios12121153","author":"S Ghosh","year":"2022","unstructured":"Ghosh, S., Kim, S.K., Ijaz, M.F., Singh, P.K., Mahmud, M.: Classification of mental stress from wearable physiological sensors using image-encoding-based deep neural network. Biosensors (2022). https:\/\/doi.org\/10.3390\/bios12121153","journal-title":"Biosensors"},{"key":"1325_CR11","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14174191","author":"F Subhan","year":"2022","unstructured":"Subhan, F., et al.: Cancerous tumor controlled treatment using search heuristic (GA)-based sliding mode and synergetic controller. Cancers (Basel) (2022). https:\/\/doi.org\/10.3390\/cancers14174191","journal-title":"Cancers (Basel)"},{"issue":"August","key":"1325_CR12","doi-asserted-by":"publisher","first-page":"94235","DOI":"10.1109\/ACCESS.2022.3203061","volume":"10","author":"J Shafi","year":"2022","unstructured":"Shafi, J., Wo\u017aniak, M., Sujatha, R.: 6G Driven fast computational networking framework for healthcare applications. IEEE Access 10(August), 94235\u201394248 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3203061","journal-title":"IEEE Access"},{"key":"1325_CR13","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/978-3-319-08927-0_21","volume":"822","author":"T Vallianatou","year":"2015","unstructured":"Vallianatou, T., Giaginis, C., Tsantili-Kakoulidou, A.: The impact of physicochemical and molecular properties in drug design: Navigation in the \u2018Drug-Like\u2019 chemical space. Adv. Exp. Med. Biol. 822, 187\u2013194 (2015). https:\/\/doi.org\/10.1007\/978-3-319-08927-0_21","journal-title":"Adv. Exp. Med. Biol."},{"key":"1325_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-1-4939-2269-7","volume-title":"Small molecule target identification using drug affinity responsive target stability (DARTS)","author":"BE Lomenick","year":"2013","unstructured":"Lomenick, B.E.: Small molecule target identification using drug affinity responsive target stability (DARTS), vol. 1263, pp. 1\u2013115. Springer, New York (2013). https:\/\/doi.org\/10.1007\/978-1-4939-2269-7"},{"issue":"3","key":"1325_CR15","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1124\/jpet.119.257113","volume":"370","author":"PM Glassman","year":"2019","unstructured":"Glassman, P.M., Muzykantov, V.R.: Pharmacokinetic and pharmacodynamic properties of drug delivery systems. J. Pharmacol Exp. Ther.Pharmacol Exp. Ther. 370(3), 570\u2013580 (2019). https:\/\/doi.org\/10.1124\/jpet.119.257113","journal-title":"J. Pharmacol Exp. Ther.Pharmacol Exp. Ther."},{"issue":"4","key":"1325_CR16","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1016\/j.jsps.2014.04.008","volume":"24","author":"TM Alshammari","year":"2016","unstructured":"Alshammari, T.M.: Drug safety: the concept, inception and its importance in patients\u2019 health. Saudi Pharm. J. 24(4), 405\u2013412 (2016). https:\/\/doi.org\/10.1016\/j.jsps.2014.04.008","journal-title":"Saudi Pharm. J."},{"issue":"20","key":"1325_CR17","doi-asserted-by":"publisher","first-page":"1755","DOI":"10.2174\/1568026618666181025114157","volume":"18","author":"AAT Naqvi","year":"2019","unstructured":"Naqvi, A.A.T., Mohammad, T., Hasan, G.M., Hassan, M.I.: Advancements in docking and molecular dynamics simulations towards ligand-receptor interactions and structure-function relationships. Curr. Top. Med. Chem.. Top. Med. Chem. 18(20), 1755\u20131768 (2019). https:\/\/doi.org\/10.2174\/1568026618666181025114157","journal-title":"Curr. Top. Med. Chem.. Top. Med. Chem."},{"issue":"1","key":"1325_CR18","doi-asserted-by":"publisher","first-page":"68","DOI":"10.4103\/0971-6580.94506","volume":"19","author":"S Rambhade","year":"2012","unstructured":"Rambhade, S., Chakarborty, A., Shrivastava, A., Patil, U.K., Rambhade, A.: A survey on polypharmacy and use of inappropriate medications. Toxicol. Int.. Int. 19(1), 68\u201373 (2012). https:\/\/doi.org\/10.4103\/0971-6580.94506","journal-title":"Toxicol. Int.. Int."},{"issue":"3","key":"1325_CR19","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.tips.2013.01.006","volume":"34","author":"B Percha","year":"2013","unstructured":"Percha, B., Altman, R.B.: Informatics confronts drug\u2013drug interactions. Trends Pharmacol. Sci.Pharmacol. Sci. 34(3), 178\u2013184 (2013). https:\/\/doi.org\/10.1016\/j.tips.2013.01.006","journal-title":"Trends Pharmacol. Sci.Pharmacol. Sci."},{"issue":"22\u201323","key":"1325_CR20","doi-asserted-by":"publisher","first-page":"5545","DOI":"10.1093\/bioinformatics\/btaa1005","volume":"36","author":"K Huang","year":"2020","unstructured":"Huang, K., Fu, T., Glass, L.M., Zitnik, M., Xiao, C., Sun, J.: DeepPurpose: A deep learning library for drug\u2013target interaction prediction. Bioinformatics 36(22\u201323), 5545\u20135547 (2020). https:\/\/doi.org\/10.1093\/bioinformatics\/btaa1005","journal-title":"Bioinformatics"},{"issue":"3","key":"1325_CR21","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1089\/cmb.2017.0135","volume":"25","author":"L Wang","year":"2018","unstructured":"Wang, L., et al.: A computational-based method for predicting drug\u2013target interactions by using stacked autoencoder deep neural network. J. Comput. Biol.Comput. Biol. 25(3), 361\u2013373 (2018). https:\/\/doi.org\/10.1089\/cmb.2017.0135","journal-title":"J. Comput. Biol.Comput. Biol."},{"issue":"March","key":"1325_CR22","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.compbiolchem.2019.03.016","volume":"80","author":"J You","year":"2019","unstructured":"You, J., Mcleod, R.D., Hu, P.: Predicting drug\u2013target interaction network using deep learning model. Comput. Biol. Chem.. Biol. Chem. 80(March), 90\u2013101 (2019). https:\/\/doi.org\/10.1016\/j.compbiolchem.2019.03.016","journal-title":"Comput. Biol. Chem.. Biol. Chem."},{"issue":"1","key":"1325_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-97193-8","volume":"11","author":"S Mei","year":"2021","unstructured":"Mei, S., Zhang, K.: A machine learning framework for predicting drug\u2013drug interactions. Sci. Rep. 11(1), 1\u201312 (2021). https:\/\/doi.org\/10.1038\/s41598-021-97193-8","journal-title":"Sci. Rep."},{"issue":"13","key":"1325_CR24","doi-asserted-by":"crossref","first-page":"i232","DOI":"10.1093\/bioinformatics\/btn162","volume":"24","author":"Y Yamanishi","year":"2008","unstructured":"Yamanishi, Y., Araki, M., Gutteridge, A., Honda, W., Kanehisa, M.: Prediction of drug\u2013target interaction networks from the integration of chemical and genomic spaces. Bioinformatics 24(13), i232\u2013i240 (2008)","journal-title":"Bioinformatics"},{"issue":"2","key":"1325_CR25","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1089\/cmb.2010.0213","volume":"18","author":"L Perlman","year":"2011","unstructured":"Perlman, L., Gottlieb, A., Atias, N., Ruppin, E., Sharan, R.: Combining drug and gene similarity measures for drug\u2013target elucidation. J. Comput. Biol.Comput. Biol. 18(2), 133\u2013145 (2011)","journal-title":"J. Comput. Biol.Comput. Biol."},{"issue":"8","key":"1325_CR26","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","volume":"27","author":"H Peng","year":"2005","unstructured":"Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell.Intell. 27(8), 1226\u20131238 (2005)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell.Intell."},{"key":"1325_CR27","doi-asserted-by":"crossref","unstructured":"Shi, J.-Y., Yiu, S.-M.: SRP: A concise non-parametric similarity-rank-based model for predicting drug\u2013target interactions. In: 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1636\u20131641 (2015)","DOI":"10.1109\/BIBM.2015.7359921"},{"key":"1325_CR28","unstructured":"Lt, K.: To err is human: building a safer health system. Inst. Med. Comm. Qual. Heal. Care Am. (2000)"},{"issue":"18","key":"1325_CR29","doi-asserted-by":"crossref","first-page":"i611","DOI":"10.1093\/bioinformatics\/bts413","volume":"28","author":"M Takarabe","year":"2012","unstructured":"Takarabe, M., Kotera, M., Nishimura, Y., Goto, S., Yamanishi, Y.: Drug target prediction using adverse event report systems: a pharmacogenomic approach. Bioinformatics 28(18), i611\u2013i618 (2012)","journal-title":"Bioinformatics"},{"issue":"6","key":"1325_CR30","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1016\/j.compbiolchem.2011.10.003","volume":"35","author":"YC Wang","year":"2011","unstructured":"Wang, Y.C., Zhang, C.H., Deng, N.Y., Wang, Y.: Kernel-based data fusion improves the drug\u2013protein interaction prediction. Comput. Biol. Chem.. Biol. Chem. 35(6), 353\u2013362 (2011). https:\/\/doi.org\/10.1016\/j.compbiolchem.2011.10.003","journal-title":"Comput. Biol. Chem.. Biol. Chem."},{"issue":"2","key":"1325_CR31","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1093\/bib\/bbu010","volume":"16","author":"T Pahikkala","year":"2015","unstructured":"Pahikkala, T., et al.: Toward more realistic drug\u2013target interaction predictions. Brief. Bioinform.Bioinform. 16(2), 325\u2013337 (2015)","journal-title":"Brief. Bioinform.Bioinform."},{"issue":"2","key":"1325_CR32","first-page":"1","volume":"4","author":"Z Xia","year":"2010","unstructured":"Xia, Z., Wu, L.-Y., Zhou, X., Wong, S.T.C.: Semi-supervised drug\u2013protein interaction prediction from heterogeneous biological spaces. BMC Syst. Biol. 4(2), 1\u201316 (2010)","journal-title":"BMC Syst. Biol."},{"issue":"19","key":"1325_CR33","doi-asserted-by":"crossref","first-page":"2149","DOI":"10.1093\/bioinformatics\/btn409","volume":"24","author":"L Jacob","year":"2008","unstructured":"Jacob, L., Vert, J.-P.: Protein-ligand interaction prediction: an improved chemogenomics approach. Bioinformatics 24(19), 2149\u20132156 (2008)","journal-title":"Bioinformatics"},{"issue":"5","key":"1325_CR34","doi-asserted-by":"crossref","first-page":"1402","DOI":"10.1021\/ci050006d","volume":"45","author":"JR Bock","year":"2005","unstructured":"Bock, J.R., Gough, D.A.: Virtual screen for ligands of orphan G protein-coupled receptors. J. Chem. Inf. Model. 45(5), 1402\u20131414 (2005)","journal-title":"J. Chem. Inf. Model."},{"issue":"3","key":"1325_CR35","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.drudis.2014.10.012","volume":"20","author":"A Lavecchia","year":"2015","unstructured":"Lavecchia, A.: Machine-learning approaches in drug discovery: methods and applications. Drug Discov. TodayDiscov. Today 20(3), 318\u2013331 (2015)","journal-title":"Drug Discov. TodayDiscov. Today"},{"issue":"3","key":"1325_CR36","volume":"5","author":"Z He","year":"2010","unstructured":"He, Z., et al.: Predicting drug\u2013target interaction networks based on functional groups and biological features. PLoS ONE 5(3), e9603 (2010)","journal-title":"PLoS ONE"},{"issue":"12","key":"1325_CR37","doi-asserted-by":"crossref","first-page":"i18","DOI":"10.1093\/bioinformatics\/btw244","volume":"32","author":"Q Yuan","year":"2016","unstructured":"Yuan, Q., Gao, J., Wu, D., Zhang, S., Mamitsuka, H., Zhu, S.: DrugE-Rank: improving drug\u2013target interaction prediction of new candidate drugs or targets by ensemble learning to rank. Bioinformatics 32(12), i18\u2013i27 (2016)","journal-title":"Bioinformatics"},{"key":"1325_CR38","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/978-1-4939-8955-3_14","volume-title":"Comput Methods Drug Repurposing","author":"A Ezzat","year":"2019","unstructured":"Ezzat, A., Wu, M., Li, X., Kwoh, C.-K.: Computational prediction of drug\u2013target interactions via ensemble learning. In: Comput Methods Drug Repurposing, pp. 239\u2013254. Springer, New York (2019)"},{"issue":"5","key":"1325_CR39","doi-asserted-by":"publisher","first-page":"445","DOI":"10.2174\/1389203718666161114111656","volume":"19","author":"L Wang","year":"2016","unstructured":"Wang, L., You, Z.-H., Chen, X., Yan, X., Liu, G., Zhang, W.: RFDT: a rotation forest-based predictor for predicting drug-target interactions using drug structure and protein sequence information. Curr. Protein Pept. Sci.. Protein Pept. Sci. 19(5), 445\u2013454 (2016). https:\/\/doi.org\/10.2174\/1389203718666161114111656","journal-title":"Curr. Protein Pept. Sci.. Protein Pept. Sci."},{"issue":"5","key":"1325_CR40","doi-asserted-by":"publisher","first-page":"468","DOI":"10.2174\/1389203718666161122103057","volume":"19","author":"Y Huang","year":"2018","unstructured":"Huang, Y., You, Z., Chen, X.: A systematic prediction of drug-target interactions using molecular fingerprints and protein sequences. Curr. Protein Pept. Sci.. Protein Pept. Sci. 19(5), 468\u2013478 (2018). https:\/\/doi.org\/10.2174\/1389203718666161122103057","journal-title":"Curr. Protein Pept. Sci.. Protein Pept. Sci."},{"issue":"1","key":"1325_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-016-0890-3","volume":"17","author":"ACA Nascimento","year":"2016","unstructured":"Nascimento, A.C.A., Prud\u00eancio, R.B.C., Costa, I.G.: A multiple kernel learning algorithm for drug\u2013target interaction prediction. BMC Bioinformatics 17(1), 1\u201316 (2016). https:\/\/doi.org\/10.1186\/s12859-016-0890-3","journal-title":"BMC Bioinformatics"},{"issue":"5","key":"1325_CR42","volume":"8","author":"F Cheng","year":"2012","unstructured":"Cheng, F., et al.: Prediction of drug\u2013target interactions and drug repositioning via network-based inference. PLoS Comput. Biol. Comput. Biol. 8(5), e1002503 (2012)","journal-title":"PLoS Comput. Biol. Comput. Biol."},{"issue":"1","key":"1325_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12967-020-02490-x","volume":"18","author":"BY Ji","year":"2020","unstructured":"Ji, B.Y., You, Z.H., Jiang, H.J., Guo, Z.H., Zheng, K.: Prediction of drug\u2013target interactions from multi-molecular network based on LINE network representation method. J. Transl. Med. 18(1), 1\u201311 (2020). https:\/\/doi.org\/10.1186\/s12967-020-02490-x","journal-title":"J. Transl. Med."},{"issue":"1","key":"1325_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13040-021-00242-1","volume":"14","author":"JY An","year":"2021","unstructured":"An, J.Y., Meng, F.R., Yan, Z.J.: An efficient computational method for predicting drug\u2013target interactions using weighted extreme learning machine and speed up robot features. BioData Min. 14(1), 1\u201317 (2021). https:\/\/doi.org\/10.1186\/s13040-021-00242-1","journal-title":"BioData Min."},{"key":"1325_CR45","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Huang, W., Zhan, X., Pan, J., Huang, Y.: An ensemble learning-based method for inferring drug\u2013target interactions combining protein sequences and drug fingerprints, vol. 2021 (2021)","DOI":"10.1155\/2021\/9933873"},{"issue":"1","key":"1325_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-021-04327-w","volume":"22","author":"Y Yue","year":"2021","unstructured":"Yue, Y., He, S.: DTI-HeNE: a novel method for drug\u2013target interaction prediction based on heterogeneous network embedding. BMC Bioinform. 22(1), 1\u201320 (2021). https:\/\/doi.org\/10.1186\/s12859-021-04327-w","journal-title":"BMC Bioinform."},{"issue":"7","key":"1325_CR47","doi-asserted-by":"crossref","first-page":"1970","DOI":"10.1039\/c2mb00002d","volume":"8","author":"X Chen","year":"2012","unstructured":"Chen, X., Liu, M.-X., Yan, G.-Y.: Drug\u2013target interaction prediction by random walk on the heterogeneous network. Mol. Biosyst.Biosyst. 8(7), 1970\u20131978 (2012)","journal-title":"Mol. Biosyst.Biosyst."},{"issue":"4","key":"1325_CR48","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1016\/j.ajhg.2008.02.013","volume":"82","author":"S K\u00f6hler","year":"2008","unstructured":"K\u00f6hler, S., Bauer, S., Horn, D., Robinson, P.N.: Walking the interactome for prioritization of candidate disease genes. Am. J. Hum. Genet. 82(4), 949\u2013958 (2008)","journal-title":"Am. J. Hum. Genet."},{"issue":"1","key":"1325_CR49","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1002\/minf.201501008","volume":"35","author":"E Gawehn","year":"2016","unstructured":"Gawehn, E., Hiss, J.A., Schneider, G.: Deep learning in drug discovery. Mol. Inform. 35(1), 3\u201314 (2016)","journal-title":"Mol. Inform."},{"issue":"11","key":"1325_CR50","doi-asserted-by":"crossref","first-page":"2594","DOI":"10.1007\/s11095-016-2029-7","volume":"33","author":"S Ekins","year":"2016","unstructured":"Ekins, S.: The next era: deep learning in pharmaceutical research. Pharm. Res. 33(11), 2594\u20132603 (2016)","journal-title":"Pharm. Res."},{"issue":"1","key":"1325_CR51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1758-2946-5-30","volume":"5","author":"F Napolitano","year":"2013","unstructured":"Napolitano, F., et al.: Drug repositioning: a machine-learning approach through data integration. J. Cheminform. 5(1), 1\u20139 (2013)","journal-title":"J. Cheminform."},{"issue":"15","key":"1325_CR52","doi-asserted-by":"crossref","first-page":"2337","DOI":"10.1093\/bioinformatics\/btx160","volume":"33","author":"N Zong","year":"2017","unstructured":"Zong, N., Kim, H., Ngo, V., Harismendy, O.: Deep mining heterogeneous networks of biomedical linked data to predict novel drug\u2013target associations. Bioinformatics 33(15), 2337\u20132344 (2017)","journal-title":"Bioinformatics"},{"issue":"Suppl 4","key":"1325_CR53","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-020-3518-6","volume":"21","author":"H Eslami Manoochehri","year":"2020","unstructured":"Eslami Manoochehri, H., Nourani, M.: Drug\u2013target interaction prediction using semi-bipartite graph model and deep learning. BMC Bioinform. 21(Suppl 4), 1\u201316 (2020). https:\/\/doi.org\/10.1186\/s12859-020-3518-6","journal-title":"BMC Bioinform."},{"issue":"6","key":"1325_CR54","volume":"15","author":"I Lee","year":"2019","unstructured":"Lee, I., Keum, J., Nam, H.: DeepConv-DTI: Prediction of drug\u2013target interactions via deep learning with convolution on protein sequences. PLoS Comput. Biol. Comput. Biol. 15(6), e1007129 (2019)","journal-title":"PLoS Comput. Biol. Comput. Biol."},{"issue":"17","key":"1325_CR55","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\u2013target binding affinity prediction. Bioinformatics 34(17), i821\u2013i829 (2018). https:\/\/doi.org\/10.1093\/bioinformatics\/bty593","journal-title":"Bioinformatics"},{"key":"1325_CR56","first-page":"3371","volume":"2018","author":"KY Gao","year":"2018","unstructured":"Gao, K.Y., Fokoue, A., Luo, H., Iyengar, A., Dey, S., Zhang, P.: Interpretable drug target prediction using deep neural representation. IJCAI 2018, 3371\u20133377 (2018)","journal-title":"IJCAI"},{"key":"1325_CR57","doi-asserted-by":"publisher","DOI":"10.1002\/minf.201900062","author":"S Redkar","year":"2020","unstructured":"Redkar, S., Mondal, S., Joseph, A., Hareesha, K.S.: A machine learning approach for drug\u2013target interaction prediction using wrapper feature selection and class balancing. Mol. Inform. (2020). https:\/\/doi.org\/10.1002\/minf.201900062","journal-title":"Mol. Inform."},{"issue":"Suppl 2","key":"1325_CR58","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-020-1052-0","volume":"20","author":"Y Wang","year":"2020","unstructured":"Wang, Y., You, Z., Yang, S., Yi, H., Chen, Z., Zheng, K.: A deep learning-based method for drug\u2013target interaction prediction based on long short-term memory neural network. BMC Med. Inform. Decis. Mak.Decis. Mak. 20(Suppl 2), 1\u20139 (2020). https:\/\/doi.org\/10.1186\/s12911-020-1052-0","journal-title":"BMC Med. Inform. Decis. Mak.Decis. Mak."},{"issue":"1","key":"1325_CR59","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-021-00552-w","volume":"13","author":"MA Thafar","year":"2021","unstructured":"Thafar, M.A., et al.: DTi2Vec: drug\u2013target interaction prediction using network embedding and ensemble learning. J. Cheminform. 13(1), 1\u201318 (2021). https:\/\/doi.org\/10.1186\/s13321-021-00552-w","journal-title":"J. Cheminform."},{"key":"1325_CR60","doi-asserted-by":"publisher","DOI":"10.1007\/s12539-021-00488-7","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., Jiang, Z., Chen, C., Wei, Q., Gu, H., Yu, B.: DeepStack\u2014DTIs\u202f: predicting drug\u2014target interactions using LightGBM feature selection and deep\u2014stacked ensemble classifi. Interdiscip. Sci. Comput. Life Sci. (2021). https:\/\/doi.org\/10.1007\/s12539-021-00488-7","journal-title":"Interdiscip. Sci. Comput. Life Sci."},{"issue":"March","key":"1325_CR61","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fcell.2022.794413","volume":"10","author":"J Sun","year":"2022","unstructured":"Sun, J., Lu, Y., Cui, L., Fu, Q., Wu, H., Chen, J.: A method of optimizing weight allocation in data integration based on Q-learning for drug-target interaction prediction. Front. Cell Dev. Biol. 10(March), 1\u201310 (2022). https:\/\/doi.org\/10.3389\/fcell.2022.794413","journal-title":"Front. Cell Dev. Biol."},{"key":"1325_CR62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-021-04366-3","volume":"22","author":"Q Ye","year":"2022","unstructured":"Ye, Q., Zhang, X., Lin, X.: Drug\u2013target interaction prediction via multiple classification strategies. BMC Bioinform. 22, 1\u201318 (2022). https:\/\/doi.org\/10.1186\/s12859-021-04366-3","journal-title":"BMC Bioinform."},{"issue":"5","key":"1325_CR63","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1021\/ci9003865","volume":"50","author":"S Agarwal","year":"2010","unstructured":"Agarwal, S., Dugar, D., Sengupta, S.: Ranking chemical structures for drug discovery: a new machine learning approach. J. Chem. Inf. Model. 50(5), 716\u2013731 (2010)","journal-title":"J. Chem. Inf. Model."},{"issue":"23\u2013581","key":"1325_CR64","first-page":"81","volume":"11","author":"CJC Burges","year":"2010","unstructured":"Burges, C.J.C.: From ranknet to lambdarank to lambdamart: an overview. Learning 11(23\u2013581), 81 (2010)","journal-title":"Learning"},{"key":"1325_CR65","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.chemolab.2017.01.016","volume":"162","author":"Q Kuang","year":"2017","unstructured":"Kuang, Q., et al.: A kernel matrix dimension reduction method for predicting drug\u2013target interaction. Chemom. Intell. Lab. Syst.. Intell. Lab. Syst. 162, 104\u2013110 (2017)","journal-title":"Chemom. Intell. Lab. Syst.. Intell. Lab. Syst."},{"issue":"1","key":"1325_CR66","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1093\/bioinformatics\/bty543","volume":"35","author":"F Wan","year":"2019","unstructured":"Wan, F., Hong, L., Xiao, A., Jiang, T., Zeng, J.: NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug\u2013target interactions. Bioinformatics 35(1), 104\u2013111 (2019)","journal-title":"Bioinformatics"},{"key":"1325_CR67","volume":"18","author":"T Ban","year":"2019","unstructured":"Ban, T., Ohue, M., Akiyama, Y.: NRLMF\u03b2: Beta-distribution-rescored neighborhood regularized logistic matrix factorization for improving the performance of drug\u2013target interaction prediction. Biochem. Biophys. Rep. 18, 100615 (2019)","journal-title":"Biochem. Biophys. Rep."},{"issue":"1","key":"1325_CR68","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1038\/s41467-017-00680-8","volume":"8","author":"Y Luo","year":"2017","unstructured":"Luo, Y., et al.: A network integration approach for drug\u2013target interaction prediction and computational drug repositioning from heterogeneous information. Nat. Commun.Commun. 8(1), 573 (2017)","journal-title":"Nat. Commun.Commun."},{"key":"1325_CR69","doi-asserted-by":"crossref","unstructured":"Zheng, X., Ding, H., Mamitsuka, H., Zhu, S.: Collaborative matrix factorization with multiple similarities for predicting drug\u2013target interactions. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1025\u20131033 (2013)","DOI":"10.1145\/2487575.2487670"},{"key":"1325_CR70","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaa044","author":"T Zhao","year":"2020","unstructured":"Zhao, T., Hu, Y., Valsdottir, L.R., Zang, T., Peng, J.: Identifying drug\u2013target interactions based on graph convolutional network and deep neural network. Brief. Bioinformat. (2020). https:\/\/doi.org\/10.1093\/bib\/bbaa044","journal-title":"Brief. Bioinformat."},{"issue":"3","key":"1325_CR71","doi-asserted-by":"publisher","first-page":"816","DOI":"10.1039\/d1sc05180f","volume":"13","author":"Z Yang","year":"2022","unstructured":"Yang, Z., Zhong, W., Zhao, L., Yu-ChianChen, C.: MGraphDTA: deep multiscale graph neural network for explainable drug\u2013target binding affinity prediction. Chem. Sci. 13(3), 816\u2013833 (2022). https:\/\/doi.org\/10.1039\/d1sc05180f","journal-title":"Chem. Sci."},{"issue":"5","key":"1325_CR72","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/bib\/bbaa430","volume":"22","author":"J Peng","year":"2021","unstructured":"Peng, J., et al.: An end-to-end heterogeneous graph representation learning-based framework for drug\u2013target interaction prediction. Brief. Bioinform.Bioinform. 22(5), 1\u20139 (2021). https:\/\/doi.org\/10.1093\/bib\/bbaa430","journal-title":"Brief. Bioinform.Bioinform."},{"issue":"2","key":"1325_CR73","doi-asserted-by":"crossref","first-page":"bbad079","DOI":"10.1093\/bib\/bbad079","volume":"24","author":"R Zhang","year":"2023","unstructured":"Zhang, R., Wang, Z., Wang, X., Meng, Z., Cui, W.: MHTAN-DTI: Metapath-based hierarchical transformer and attention network for drug\u2013target interaction prediction. Brief. Bioinform.Bioinform. 24(2), bbad079 (2023)","journal-title":"Brief. Bioinform.Bioinform."},{"key":"1325_CR74","doi-asserted-by":"crossref","unstructured":"Boezer, M, Tavakol, M., Sajadi, Z.: FastDTI: drug\u2013target interaction prediction using multimodality and transformers. In: Proceedings of the Northern Lights Deep Learning Workshop, vol. 4 (2023)","DOI":"10.7557\/18.6788"},{"key":"1325_CR75","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1016\/j.neunet.2023.11.018","volume":"169","author":"H Wu","year":"2024","unstructured":"Wu, H., et al.: AttentionMGT-DTA: a multi-modal drug\u2013target affinity prediction using graph transformer and attention mechanism. Neural Netw.Netw. 169, 623\u2013636 (2024)","journal-title":"Neural Netw.Netw."},{"key":"1325_CR76","unstructured":"Liu, J., et al.: Drug\u2013target interaction prediction via combining transformer and graph neural networks"},{"key":"1325_CR77","doi-asserted-by":"crossref","unstructured":"Feng, Y., Zhang, S.: Yue-Hua\u2014DPDDI a deep predictor for drug\u2013drug interactions.pdf, pp. 1\u201315 (2020)","DOI":"10.1186\/s12859-020-03724-x"},{"issue":"6","key":"1325_CR78","doi-asserted-by":"crossref","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\u2014drug interaction through molecular structure similarity analysis. J. Am. Med. Informat. Assoc. 19(6), 1066\u20131074 (2012)","journal-title":"J. Am. Med. Informat. Assoc."},{"key":"1325_CR79","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.jbi.2017.04.021","volume":"70","author":"R Ferdousi","year":"2017","unstructured":"Ferdousi, R., Safdari, R., Omidi, Y.: Computational prediction of drug\u2013drug interactions based on drugs functional similarities. J. Biomed. Inform. 70, 54\u201364 (2017)","journal-title":"J. Biomed. Inform."},{"issue":"1","key":"1325_CR80","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1038\/msb.2012.26","volume":"8","author":"A Gottlieb","year":"2012","unstructured":"Gottlieb, A., Stein, G.Y., Oron, Y., Ruppin, E., Sharan, R.: INDI: a computational framework for inferring drug interactions and their associated recommendations. Mol. Syst. Biol. 8(1), 592 (2012)","journal-title":"Mol. Syst. Biol."},{"issue":"12","key":"1325_CR81","doi-asserted-by":"crossref","first-page":"2007","DOI":"10.1093\/bioinformatics\/btv080","volume":"31","author":"P Li","year":"2015","unstructured":"Li, P., et al.: Large-scale exploration and analysis of drug combinations. Bioinformatics 31(12), 2007\u20132016 (2015)","journal-title":"Bioinformatics"},{"key":"1325_CR82","doi-asserted-by":"crossref","unstructured":"Shi, J.-Y., Gao, K., Shang, X.-Q., Yiu, S.-M.: LCM-DS: a novel approach of predicting drug\u2013drug interactions for new drugs via Dempster-Shafer theory of evidence. In: 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 512\u2013515 (2016)","DOI":"10.1109\/BIBM.2016.7822571"},{"issue":"017","key":"1325_CR83","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.: Data and text mining drug\u2013drug interaction extraction via hierarchical RNNs on sequence and shortest dependency paths. Bioinformatics 34(017), 828\u2013835 (2018). https:\/\/doi.org\/10.1093\/bioinformatics\/btx659","journal-title":"Bioinformatics"},{"issue":"1","key":"1325_CR84","doi-asserted-by":"publisher","first-page":"1","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 13(1), 1\u201317 (2018). https:\/\/doi.org\/10.1371\/journal.pone.0190926","journal-title":"PLoS ONE"},{"key":"1325_CR85","doi-asserted-by":"publisher","DOI":"10.3390\/e21010037","author":"F Loss","year":"2019","unstructured":"Loss, F., et al.: Drug\u2013drug interaction extraction via recurrent hybrid convolutional neural networks with. Entropy (2019). https:\/\/doi.org\/10.3390\/e21010037","journal-title":"Entropy"},{"key":"1325_CR86","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.ins.2017.06.021","volume":"415\u2013416","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. (Ny) 415\u2013416, 100\u2013109 (2017). https:\/\/doi.org\/10.1016\/j.ins.2017.06.021","journal-title":"Inf. Sci. (Ny)"},{"issue":"5","key":"1325_CR87","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1093\/bib\/bbx017","volume":"19","author":"M Lotfi Shahreza","year":"2018","unstructured":"Lotfi Shahreza, M., Ghadiri, N., Mousavi, S.R., Varshosaz, J., Green, J.R.: A review of network-based approaches to drug repositioning. Brief. Bioinform.Bioinform. 19(5), 878\u2013892 (2018)","journal-title":"Brief. Bioinform.Bioinform."},{"key":"1325_CR88","doi-asserted-by":"publisher","DOI":"10.1186\/s12918-016-0311-2","author":"Y Zhang","year":"2016","unstructured":"Zhang, Y., et al.: Leveraging syntactic and semantic graph kernels to extract pharmacokinetic drug drug interactions from biomedical literature. BMC Syst. Biol. (2016). https:\/\/doi.org\/10.1186\/s12918-016-0311-2","journal-title":"BMC Syst. Biol."},{"key":"1325_CR89","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.jbi.2016.03.014","volume":"61","author":"W Zheng","year":"2016","unstructured":"Zheng, W., et al.: A graph kernel based on context vectors for extracting drug\u2013drug interactions. J. Biomed. Inform. 61, 34\u201343 (2016). https:\/\/doi.org\/10.1016\/j.jbi.2016.03.014","journal-title":"J. Biomed. Inform."},{"key":"1325_CR90","doi-asserted-by":"publisher","DOI":"10.3390\/life12020319","author":"X Han","year":"2022","unstructured":"Han, X., Xie, R., Li, X., Li, J.: SmileGNN: drug-drug interaction prediction based on the SMILES and graph neural network. Life (2022). https:\/\/doi.org\/10.3390\/life12020319","journal-title":"Life"},{"issue":"3","key":"1325_CR91","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0058321","volume":"8","author":"S Vilar","year":"2013","unstructured":"Vilar, S., Uriarte, E., Santana, L., Tatonetti, N.P., Friedman, C.: Detection of drug\u2013drug interactions by modeling interaction profile fingerprints. PLoS ONE 8(3), e58321 (2013)","journal-title":"PLoS ONE"},{"key":"1325_CR92","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13721-019-0207-3","volume":"9","author":"N Rohani","year":"2020","unstructured":"Rohani, N., Eslahchi, C., Katanforoush, A.: ISCMF: Integrated similarity-constrained matrix factorization for drug\u2013drug interaction prediction. Netw. Model. Anal. Heal. Informat. Bioinform. 9, 1\u20138 (2020)","journal-title":"Netw. Model. Anal. Heal. Informat. Bioinform."},{"issue":"8","key":"1325_CR93","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0219796","volume":"14","author":"G Shtar","year":"2019","unstructured":"Shtar, G., Rokach, L., Shapira, B.: Detecting drug\u2013drug interactions using artificial neural networks and classic graph similarity measures. PLoS ONE 14(8), e0219796 (2019)","journal-title":"PLoS ONE"},{"issue":"1","key":"1325_CR94","first-page":"101","volume":"12","author":"H Yu","year":"2018","unstructured":"Yu, H., et al.: Predicting and understanding comprehensive drug\u2013drug interactions via semi-nonnegative matrix factorization. BMC Syst. Biol. 12(1), 101\u2013110 (2018)","journal-title":"BMC Syst. Biol."},{"issue":"14","key":"1325_CR95","first-page":"27","volume":"19","author":"J-Y Shi","year":"2018","unstructured":"Shi, J.-Y., et al.: TMFUF: a triple matrix factorization-based unified framework for predicting comprehensive drug\u2013drug interactions of new drugs. BMC Bioinform. 19(14), 27\u201337 (2018)","journal-title":"BMC Bioinform."},{"issue":"1","key":"1325_CR96","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-019-0352-9","volume":"11","author":"JY Shi","year":"2019","unstructured":"Shi, J.Y., Mao, K.T., Yu, H., Yiu, S.M.: Detecting drug communities and predicting comprehensive drug\u2013drug interactions via balance regularized semi-nonnegative matrix factorization. J. Cheminform. 11(1), 1\u201316 (2019). https:\/\/doi.org\/10.1186\/s13321-019-0352-9","journal-title":"J. Cheminform."},{"issue":"1","key":"1325_CR97","doi-asserted-by":"crossref","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\u2013drug interactions based on clinical side effects. Sci. Rep. 5(1), 12339 (2015)","journal-title":"Sci. Rep."},{"key":"1325_CR98","first-page":"1","volume":"18","author":"W Zhang","year":"2017","unstructured":"Zhang, W., Chen, Y., Liu, F., Luo, F., Tian, G., Li, X.: Predicting potential drug\u2013drug interactions by integrating chemical, biological, phenotypic and network data. BMC Bioinform. 18, 1\u201312 (2017)","journal-title":"BMC Bioinform."},{"key":"1325_CR99","doi-asserted-by":"publisher","DOI":"10.1136\/amiajnl-2013-002512","author":"F Cheng","year":"2014","unstructured":"Cheng, F., Zhao, Z.: \u201cMachine learning-based prediction of drug\u2013drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. J. Am. Med. Informat. Assoc. (2014). https:\/\/doi.org\/10.1136\/amiajnl-2013-002512","journal-title":"J. Am. Med. Informat. Assoc."},{"issue":"June","key":"1325_CR100","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.jbi.2018.06.015","volume":"84","author":"SS Deepika","year":"2018","unstructured":"Deepika, S.S., Geetha, T.V.: A meta-learning framework using representation learning to predict drug\u2013drug interaction. J. Biomed. Inform. 84(June), 136\u2013147 (2018). https:\/\/doi.org\/10.1016\/j.jbi.2018.06.015","journal-title":"J. Biomed. Inform."},{"key":"1325_CR101","doi-asserted-by":"crossref","unstructured":"Wang, T. et al.: A multi-scale feature fusion model based on biological knowledge graph and transformer-encoder for drug\u2013drug interaction prediction. bioRxiv, pp. 2001\u20132024 (2024)","DOI":"10.1101\/2024.01.12.575305"},{"issue":"18","key":"1325_CR102","doi-asserted-by":"crossref","first-page":"i547","DOI":"10.1093\/bioinformatics\/btq382","volume":"26","author":"L Tari","year":"2010","unstructured":"Tari, L., Anwar, S., Liang, S., Cai, J., Baral, C.: Discovering drug\u2013drug interactions: a text-mining and reasoning approach based on properties of drug metabolism. Bioinformatics 26(18), i547\u2013i553 (2010)","journal-title":"Bioinformatics"},{"issue":"125","key":"1325_CR103","doi-asserted-by":"crossref","first-page":"125ra31","DOI":"10.1126\/scitranslmed.3003377","volume":"4","author":"NP Tatonetti","year":"2012","unstructured":"Tatonetti, N.P., Ye, P.P., Daneshjou, R., Altman, R.B.: Data-driven prediction of drug effects and interactions. Sci. Transl. Med. 4(125), 125ra31-125ra31 (2012)","journal-title":"Sci. Transl. Med."},{"key":"1325_CR104","first-page":"409","volume":"2013","author":"A Kolchinsky","year":"2013","unstructured":"Kolchinsky, A., Louren\u00e7o, A., Li, L., Rocha, L.M.: Evaluation of linear classifiers on articles containing pharmacokinetic evidence of drug\u2013drug interactions. Biocomput. World Sci. 2013, 409\u2013420 (2013)","journal-title":"Biocomput. World Sci."},{"key":"1325_CR105","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/6918381","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.. Math. Methods Med. (2016). https:\/\/doi.org\/10.1155\/2016\/6918381","journal-title":"Comput. Math. Methods Med.. Math. Methods Med."},{"key":"1325_CR106","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-022-04612-2","volume":"23","author":"C Zhang","year":"2022","unstructured":"Zhang, C., Lu, Y., Zang, T.: CNN-DDI: a learning-based method for predicting drug\u2013drug interactions using convolution neural networks. BMC Bioinform. 23, 1\u201312 (2022). https:\/\/doi.org\/10.1186\/s12859-022-04612-2","journal-title":"BMC Bioinform."},{"issue":"1","key":"1325_CR107","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-020-03950-3","volume":"22","author":"TN Jarada","year":"2021","unstructured":"Jarada, T.N., Rokne, J.G., Alhajj, R.: SNF-NN: computational method to predict drug\u2013disease interactions using similarity network fusion and neural networks. BMC Bioinform. 22(1), 1\u201320 (2021)","journal-title":"BMC Bioinform."},{"issue":"1","key":"1325_CR108","doi-asserted-by":"crossref","first-page":"6775","DOI":"10.1038\/s41467-021-27137-3","volume":"12","author":"Q Ye","year":"2021","unstructured":"Ye, Q., et al.: A unified drug\u2013target interaction prediction framework based on knowledge graph and recommendation system. Nat. Commun.Commun. 12(1), 6775 (2021)","journal-title":"Nat. Commun.Commun."},{"issue":"17","key":"1325_CR109","doi-asserted-by":"publisher","first-page":"2664","DOI":"10.1093\/bioinformatics\/btw228","volume":"32","author":"H Luo","year":"2016","unstructured":"Luo, H., et al.: Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm. Bioinformatics 32(17), 2664\u20132671 (2016). https:\/\/doi.org\/10.1093\/bioinformatics\/btw228","journal-title":"Bioinformatics"},{"issue":"1","key":"1325_CR110","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-018-2220-4","volume":"19","author":"W Zhang","year":"2018","unstructured":"Zhang, W., et al.: Predicting drug\u2013disease associations by using similarity constrained matrix factorization. BMC Bioinformat. 19(1), 1\u201312 (2018). https:\/\/doi.org\/10.1186\/s12859-018-2220-4","journal-title":"BMC Bioinformat."},{"issue":"1","key":"1325_CR111","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-61616-9","volume":"10","author":"HJ Jiang","year":"2020","unstructured":"Jiang, H.J., Huang, Y.A., You, Z.H.: SAEROF: an ensemble approach for large-scale drug\u2013disease association prediction by incorporating rotation forest and sparse autoencoder deep neural network. Sci. Rep. 10(1), 1\u201311 (2020). https:\/\/doi.org\/10.1038\/s41598-020-61616-9","journal-title":"Sci. Rep."},{"key":"1325_CR112","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1752-0509-7-1","volume":"7","author":"C Wu","year":"2013","unstructured":"Wu, C., Gudivada, R.C., Aronow, B.J., Jegga, A.G.: Computational drug repositioning through heterogeneous network clustering. BMC Syst. Biol. 7, 1\u20139 (2013)","journal-title":"BMC Syst. Biol."},{"issue":"20","key":"1325_CR113","doi-asserted-by":"publisher","first-page":"2923","DOI":"10.1093\/bioinformatics\/btu403","volume":"30","author":"W Wang","year":"2014","unstructured":"Wang, W., Yang, S., Zhang, X., Li, J.: Drug repositioning by integrating target information through a heterogeneous network model. Bioinformatics 30(20), 2923\u20132930 (2014). https:\/\/doi.org\/10.1093\/bioinformatics\/btu403","journal-title":"Bioinformatics"},{"issue":"1","key":"1325_CR114","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.artmed.2014.11.003","volume":"63","author":"V Martinez","year":"2015","unstructured":"Martinez, V., Navarro, C., Cano, C., Fajardo, W., Blanco, A.: DrugNet: network-based drug\u2013disease prioritization by integrating heterogeneous data. Artif. Intell. Med.. Intell. Med. 63(1), 41\u201349 (2015)","journal-title":"Artif. Intell. Med.. Intell. Med."},{"key":"1325_CR115","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1186\/s12859-016-1096-4","volume":"17","author":"H Liu","year":"2016","unstructured":"Liu, H., Song, Y., Guan, J., Luo, L., Zhuang, Z.: Inferring new indications for approved drugs via random walk on drug\u2013disease heterogenous networks. BMC Bioinform. 17, 269\u2013277 (2016)","journal-title":"BMC Bioinform."},{"issue":"15","key":"1325_CR116","first-page":"1","volume":"20","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Guo, M., Ren, Y., Jia, L., Yu, G.: Drug repositioning based on individual bi-random walks on a heterogeneous network. BMC Bioinform. 20(15), 1\u201313 (2019)","journal-title":"BMC Bioinform."},{"issue":"3","key":"1325_CR117","first-page":"1","volume":"20","author":"G Wu","year":"2019","unstructured":"Wu, G., Liu, J., Yue, X.: Prediction of drug\u2013disease associations based on ensemble meta paths and singular value decomposition. BMC Bioinform. 20(3), 1\u201313 (2019)","journal-title":"BMC Bioinform."},{"issue":"6","key":"1325_CR118","doi-asserted-by":"crossref","first-page":"997","DOI":"10.1039\/D1MO00237F","volume":"17","author":"G Xie","year":"2021","unstructured":"Xie, G., et al.: Bgmsdda: a bipartite graph diffusion algorithm with multiple similarity integration for drug\u2013disease association prediction. Mol. Omi. 17(6), 997\u20131011 (2021)","journal-title":"Mol. Omi."},{"key":"1325_CR119","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2021.657182","author":"BW Zhao","year":"2021","unstructured":"Zhao, B.W., You, Z.H., Wong, L., Zhang, P., Li, H.Y., Wang, L.: MGRL: predicting drug-disease associations based on multi-graph representation learning. Front. Genet. (2021). https:\/\/doi.org\/10.3389\/fgene.2021.657182","journal-title":"Front. Genet."},{"issue":"1","key":"1325_CR120","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-020-03950-3","volume":"22","author":"TN Jarada","year":"2021","unstructured":"Jarada, T.N., Rokne, J.G., Alhajj, R.: SNF-NN: computational method to predict drug\u2013disease interactions using similarity network fusion and neural networks. BMC Bioinform. 22(1), 1\u201320 (2021). https:\/\/doi.org\/10.1186\/s12859-020-03950-3","journal-title":"BMC Bioinform."},{"issue":"11","key":"1325_CR121","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1093\/bioinformatics\/bty013","volume":"34","author":"H Luo","year":"2018","unstructured":"Luo, H., Li, M., Wang, S., Liu, Q., Li, Y., Wang, J.: Computational drug repositioning using low-rank matrix approximation and randomized algorithms. Bioinformatics 34(11), 1904\u20131912 (2018)","journal-title":"Bioinformatics"},{"issue":"12","key":"1325_CR122","volume":"15","author":"M Yang","year":"2019","unstructured":"Yang, M., Luo, H., Li, Y., Wu, F.-X., Wang, J.: Overlap matrix completion for predicting drug\u2013associated indications. PLoS Comput. Biol. Comput. Biol. 15(12), e1007541 (2019)","journal-title":"PLoS Comput. Biol. Comput. Biol."},{"issue":"9","key":"1325_CR123","doi-asserted-by":"crossref","first-page":"2839","DOI":"10.1093\/bioinformatics\/btaa062","volume":"36","author":"W Zhang","year":"2020","unstructured":"Zhang, W., Xu, H., Li, X., Gao, Q., Wang, L.: DRIMC: an improved drug repositioning approach using Bayesian inductive matrix completion. Bioinformatics 36(9), 2839\u20132847 (2020)","journal-title":"Bioinformatics"},{"issue":"4","key":"1325_CR124","doi-asserted-by":"crossref","first-page":"bbaa267","DOI":"10.1093\/bib\/bbaa267","volume":"22","author":"M Yang","year":"2021","unstructured":"Yang, M., Wu, G., Zhao, Q., Li, Y., Wang, J.: Computational drug repositioning based on multi-similarities bilinear matrix factorization. Brief. Bioinform.Bioinform. 22(4), bbaa267 (2021)","journal-title":"Brief. Bioinform.Bioinform."},{"issue":"7","key":"1325_CR125","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0270852","volume":"17","author":"AA Jamali","year":"2022","unstructured":"Jamali, A.A., Tan, Y., Kusalik, A., Wu, F.-X.: NTD-DR: nonnegative tensor decomposition for drug repositioning. PLoS ONE 17(7), e0270852 (2022)","journal-title":"PLoS ONE"},{"issue":"24","key":"1325_CR126","doi-asserted-by":"crossref","first-page":"5191","DOI":"10.1093\/bioinformatics\/btz418","volume":"35","author":"X Zeng","year":"2019","unstructured":"Zeng, X., Zhu, S., Liu, X., Zhou, Y., Nussinov, R., Cheng, F.: deepDR: a network-based deep learning approach to in silico drug repositioning. Bioinformatics 35(24), 5191\u20135198 (2019)","journal-title":"Bioinformatics"},{"key":"1325_CR127","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-018-2565-8","volume":"20","author":"X Yang","year":"2019","unstructured":"Yang, X., Zamit, L., Liu, Y., He, J.: Additional neural matrix factorization model for computational drug repositioning. BMC Bioinform. 20, 1\u201311 (2019)","journal-title":"BMC Bioinform."},{"issue":"13","key":"1325_CR128","first-page":"1","volume":"21","author":"R Zhou","year":"2020","unstructured":"Zhou, R., Lu, Z., Luo, H., Xiang, J., Zeng, M., Li, M.: NEDD: a network embedding based method for predicting drug\u2013disease associations. BMC Bioinform. 21(13), 1\u201312 (2020)","journal-title":"BMC Bioinform."},{"issue":"4","key":"1325_CR129","doi-asserted-by":"crossref","first-page":"bbaa243","DOI":"10.1093\/bib\/bbaa243","volume":"22","author":"Z Yu","year":"2021","unstructured":"Yu, Z., Huang, F., Zhao, X., Xiao, W., Zhang, W.: Predicting drug\u2013disease associations through layer attention graph convolutional network. Brief. Bioinform.Bioinform. 22(4), bbaa243 (2021)","journal-title":"Brief. Bioinform.Bioinform."},{"key":"1325_CR130","doi-asserted-by":"publisher","DOI":"10.1186\/s12967-019-2127-5","author":"J Transl","year":"2019","unstructured":"Transl, J., Jiang, H.J., You, Z.H., Huang, Y.A.: Predicting drug\u2013disease associations via sigmoid kernel-based convolutional neural networks. J. Transl. Med. (2019). https:\/\/doi.org\/10.1186\/s12967-019-2127-5","journal-title":"J. Transl. Med."},{"key":"1325_CR131","doi-asserted-by":"crossref","first-page":"2712","DOI":"10.3390\/molecules24152712","volume":"24","author":"C. N. Network and R. Unit","year":"2019","unstructured":"C. N. Network and R. Unit: Inferring drug-related diseases based on convolutional neural network and gated. Molecules 24, 2712 (2019)","journal-title":"Molecules"},{"issue":"1","key":"1325_CR132","doi-asserted-by":"publisher","first-page":"November","DOI":"10.3389\/fphar.2019.01301","volume":"10","author":"P Xuan","year":"2019","unstructured":"Xuan, P., Cui, H., Shen, T., Sheng, N., Zhang, T.: HeteroDualNet\u202f: a dual convolutional neural network with heterogeneous layers for drug-disease association prediction via chou \u2018 s five-step rule. Front. Pharmacol.Pharmacol. 10(1), November-12 (2019). https:\/\/doi.org\/10.3389\/fphar.2019.01301","journal-title":"Front. Pharmacol.Pharmacol."},{"key":"1325_CR133","doi-asserted-by":"publisher","DOI":"10.3389\/fchem.2019.00924","author":"Z Li","year":"2020","unstructured":"Li, Z., Huang, Q., Chen, X., Wang, Y., Li, J., Xie, Y.: Identification of drug-disease associations using information of molecular structures and clinical symptoms via deep convolutional neural network. Front. Chem. (2020). https:\/\/doi.org\/10.3389\/fchem.2019.00924","journal-title":"Front. Chem."},{"issue":"1","key":"1325_CR134","doi-asserted-by":"publisher","first-page":"7419","DOI":"10.3934\/mbe.2021367","volume":"18","author":"H Wang","year":"2021","unstructured":"Wang, H., Zhao, S., Zhao, J., Feng, Z.: A model for predicting drug\u2013disease associations based on dense convolutional attention network. Math. Biosci. Eng.Biosci. Eng. 18(1), 7419\u20137439 (2021). https:\/\/doi.org\/10.3934\/mbe.2021367","journal-title":"Math. Biosci. Eng.Biosci. Eng."},{"issue":"Suppl 9","key":"1325_CR135","first-page":"123","volume":"12","author":"Z Tian","year":"2018","unstructured":"Tian, Z., Teng, Z., Cheng, S., Guo, M.: Computational drug repositioning using meta-path-based semantic network analysis. BMC Syst. Biol. 12(Suppl 9), 123 (2018)","journal-title":"BMC Syst. Biol."},{"issue":"4","key":"1325_CR136","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1007\/s10441-018-9325-z","volume":"66","author":"DH Le","year":"2018","unstructured":"Le, D.H., Nguyen-Ngoc, D.: Drug repositioning by integrating known disease-gene and drug\u2013target associations in a semi-supervised learning model. Acta Biotheor. Biotheor. 66(4), 315\u2013331 (2018). https:\/\/doi.org\/10.1007\/s10441-018-9325-z","journal-title":"Acta Biotheor. Biotheor."},{"key":"1325_CR137","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-020-03882-y","volume":"22","author":"HC Yi","year":"2021","unstructured":"Yi, H.C., You, Z.H., Wang, L., Su, X.R., Zhou, X., Jiang, T.H.: In silico drug repositioning using deep learning and comprehensive similarity measures. BMC Bioinform. 22, 1\u201314 (2021). https:\/\/doi.org\/10.1186\/s12859-020-03882-y","journal-title":"BMC Bioinform."},{"key":"1325_CR138","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2018.03.011","author":"Y Zhang","year":"2018","unstructured":"Zhang, Y., Lin, H., Yang, Z., Wang, J., Zhang, S.: A hybrid model based on neural networks for biomedical relation extraction. J. Biomed. Inform. (2018). https:\/\/doi.org\/10.1016\/j.jbi.2018.03.011","journal-title":"J. Biomed. Inform."},{"issue":"4","key":"1325_CR139","doi-asserted-by":"publisher","first-page":"688","DOI":"10.1016\/j.cell.2020.01.021","volume":"180","author":"JM Stokes","year":"2020","unstructured":"Stokes, J.M., et al.: A deep learning approach to antibiotic discovery. Cell 180(4), 688-702.e13 (2020). https:\/\/doi.org\/10.1016\/j.cell.2020.01.021","journal-title":"Cell"},{"key":"1325_CR140","doi-asserted-by":"publisher","unstructured":"Li, J., Lu, Z.: A new method for computational drug repositioning using drug pairwise similarity. In: Proc.\u20142012 IEEE Int. Conf. Bioinforma. Biomed. BIBM 2012, no. 1, pp. 453\u2013456 (2012). https:\/\/doi.org\/10.1109\/BIBM.2012.6392722","DOI":"10.1109\/BIBM.2012.6392722"},{"issue":"3","key":"1325_CR141","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/bib\/bbaa110","volume":"22","author":"H Fei","year":"2021","unstructured":"Fei, H., Ren, Y., Zhang, Y., Ji, D., Liang, X.: Enriching contextualized language model from knowledge graph for biomedical information extraction. Brief. Bioinform.Bioinform. 22(3), 1\u201314 (2021). https:\/\/doi.org\/10.1093\/bib\/bbaa110","journal-title":"Brief. Bioinform.Bioinform."},{"key":"1325_CR142","doi-asserted-by":"crossref","first-page":"65711","DOI":"10.1109\/ACCESS.2023.3289863","volume":"11","author":"S Mam","year":"2023","unstructured":"Mam, S., Wichadakul, D., Vateekul, P.: Drug repurposing for type 2 diabetes using combined textual and structural graph representation based on transformer. IEEE Access 11, 65711 (2023)","journal-title":"IEEE Access"},{"key":"1325_CR143","volume":"168","author":"Y Liu","year":"2024","unstructured":"Liu, Y., Sang, G., Liu, Z., Pan, Y., Cheng, J., Zhang, Y.: MPTN: a message-passing transformer network for drug repurposing from knowledge graph. Comput. Biol. Med.. Biol. Med. 168, 107800 (2024)","journal-title":"Comput. Biol. Med.. Biol. Med."},{"key":"1325_CR144","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty294","author":"M Zitnik","year":"2018","unstructured":"Zitnik, M., Agrawal, M., Leskovec, J., Science, C., Biohub, C.Z.: Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics (2018). https:\/\/doi.org\/10.1093\/bioinformatics\/bty294","journal-title":"Bioinformatics"},{"issue":"1","key":"1325_CR145","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-021-04298-y","volume":"22","author":"R Masumshah","year":"2021","unstructured":"Masumshah, R., Aghdam, R., Eslahchi, C.: A neural network-based method for polypharmacy side effects prediction. BMC Bioinform. 22(1), 1\u201317 (2021). https:\/\/doi.org\/10.1186\/s12859-021-04298-y","journal-title":"BMC Bioinform."},{"key":"1325_CR146","unstructured":"Open Research Online (2016)"},{"issue":"2","key":"1325_CR147","doi-asserted-by":"publisher","first-page":"1679","DOI":"10.1093\/bib\/bbaa012","volume":"22","author":"SK Mohamed","year":"2021","unstructured":"Mohamed, S.K., Nounu, A., Nov\u00e1\u010dek, V.: Biological applications of knowledge graph embedding models. Brief. Bioinform.Bioinform. 22(2), 1679\u20131693 (2021). https:\/\/doi.org\/10.1093\/bib\/bbaa012","journal-title":"Brief. Bioinform.Bioinform."},{"key":"1325_CR148","first-page":"1","volume":"2021","author":"S Liu","year":"2021","unstructured":"Liu, S., An, J., Zhao, J., Zhao, S., Lv, H., Wang, S.: Drug\u2013Target interaction prediction based on multisource information weighted fusion. Contrast Media Mol. Imaging 2021, 1 (2021)","journal-title":"Contrast Media Mol. Imaging"},{"issue":"19","key":"1325_CR149","first-page":"267","volume":"17","author":"A Ezzat","year":"2016","unstructured":"Ezzat, A., Wu, M., Li, X.-L., Kwoh, C.-K.: Drug\u2013target interaction prediction via class imbalance-aware ensemble learning. BMC Bioinform. 17(19), 267\u2013276 (2016)","journal-title":"BMC Bioinform."},{"issue":"1","key":"1325_CR150","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13036-022-00296-7","volume":"16","author":"H El-Behery","year":"2022","unstructured":"El-Behery, H., Attia, A.-F., El-Fishawy, N., Torkey, H.: An ensemble-based drug\u2013target interaction prediction approach using multiple feature information with data balancing. J. Biol. Eng. 16(1), 1\u201314 (2022)","journal-title":"J. Biol. Eng."},{"issue":"6","key":"1325_CR151","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1002\/cpt.1434","volume":"105","author":"J Niu","year":"2019","unstructured":"Niu, J., Straubinger, R.M., Mager, D.E.: Pharmacodynamic drug\u2013drug interactions. Clin. Pharmacol. Ther.. Pharmacol. Ther. 105(6), 1395\u20131406 (2019)","journal-title":"Clin. Pharmacol. Ther.. Pharmacol. Ther."},{"key":"1325_CR152","doi-asserted-by":"crossref","first-page":"1176400","DOI":"10.3389\/fnagi.2023.1176400","volume":"15","author":"L Zhou","year":"2023","unstructured":"Zhou, L., Wang, Y., Peng, L., Li, Z., Luo, X.: Identifying potential drug\u2013target interactions based on ensemble deep learning. Front. Aging Neurosci. 15, 1176400 (2023)","journal-title":"Front. Aging Neurosci."},{"key":"1325_CR153","volume":"12","author":"K Han","year":"2022","unstructured":"Han, K., et al.: A review of approaches for predicting drug\u2013drug interactions based on machine learning. Front. Pharmacol.Pharmacol. 12, 814858 (2022)","journal-title":"Front. Pharmacol.Pharmacol."},{"issue":"10","key":"1325_CR154","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1038\/s42256-020-00236-4","volume":"2","author":"J Jim\u00e9nez-Luna","year":"2020","unstructured":"Jim\u00e9nez-Luna, J., Grisoni, F., Schneider, G.: Drug discovery with explainable artificial intelligence. Nat. Mach. Intell. 2(10), 573\u2013584 (2020)","journal-title":"Nat. Mach. Intell."}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-024-01325-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-024-01325-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-024-01325-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T06:26:16Z","timestamp":1731738376000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-024-01325-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,14]]},"references-count":154,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["1325"],"URL":"https:\/\/doi.org\/10.1007\/s00530-024-01325-9","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,14]]},"assertion":[{"value":"18 January 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 April 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors hereby declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"124"}}