{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T09:58:20Z","timestamp":1780480700165,"version":"3.54.1"},"reference-count":69,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T00:00:00Z","timestamp":1724716800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T00:00:00Z","timestamp":1724716800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nat Mach Intell"],"DOI":"10.1038\/s42256-024-00888-6","type":"journal-article","created":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T10:02:47Z","timestamp":1724752967000},"page":"1094-1105","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Learning motif-based graphs for drug\u2013drug interaction prediction via local\u2013global self-attention"],"prefix":"10.1038","volume":"6","author":[{"given":"Yi","family":"Zhong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gaozheng","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Houbing","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongqiang","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiheng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5192-8878","authenticated-orcid":false,"given":"Heng","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6253-2713","authenticated-orcid":false,"given":"Biao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1089-8673","authenticated-orcid":false,"given":"Zuquan","family":"Weng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,27]]},"reference":[{"key":"888_CR1","first-page":"i","volume":"6","author":"RJ Dagli","year":"2014","unstructured":"Dagli, R. J. & Sharma, A. Polypharmacy: a global risk factor for elderly people. J. Int. Oral Health 6, i\u2013ii (2014).","journal-title":"J. Int. Oral Health"},{"key":"888_CR2","doi-asserted-by":"publisher","first-page":"85","DOI":"10.3390\/geriatrics5040085","volume":"5","author":"P Aggarwal","year":"2020","unstructured":"Aggarwal, P., Woolford, S. J. & Patel, H. P. Multi-morbidity and polypharmacy in older people: challenges and opportunities for clinical practice. Geriatrics 5, 85 (2020).","journal-title":"Geriatrics"},{"key":"888_CR3","doi-asserted-by":"publisher","DOI":"10.3389\/fphar.2022.923939","volume":"13","author":"H Jiang","year":"2022","unstructured":"Jiang, H. et al. Adverse drug reactions and correlations with drug-drug interactions: a retrospective study of reports from 2011 to 2020. Front. Pharmacol. 13, 923939 (2022).","journal-title":"Front. Pharmacol."},{"key":"888_CR4","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1007\/s41066-022-00315-4","volume":"8","author":"X Hao","year":"2023","unstructured":"Hao, X. et al. Enhancing drug-drug interaction prediction by three-way decision and knowledge graph embedding. Granul. Comput. 8, 67\u201376 (2023).","journal-title":"Granul. Comput."},{"key":"888_CR5","doi-asserted-by":"publisher","first-page":"8693","DOI":"10.1039\/D2SC02023H","volume":"13","author":"Z Yang","year":"2022","unstructured":"Yang, Z., Zhong, W., Lv, Q. & Yu-Chian Chen, C. Learning size-adaptive molecular substructures for explainable drug-drug interaction prediction by substructure-aware graph neural network. Chem. Sci. 13, 8693\u20138703 (2022).","journal-title":"Chem. Sci."},{"key":"888_CR6","doi-asserted-by":"crossref","unstructured":"Zhang, X. et al. Molormer: a lightweight self-attention-based method focused on spatial structure of molecular graph for drug-drug interactions prediction. Brief. Bioinform. 23, bbac296 (2022).","DOI":"10.1093\/bib\/bbac296"},{"key":"888_CR7","doi-asserted-by":"publisher","first-page":"e4304","DOI":"10.1073\/pnas.1803294115","volume":"115","author":"JY Ryu","year":"2018","unstructured":"Ryu, J. Y., Kim, H. U. & Lee, S. Y. Deep learning improves prediction of drug-drug and drug-food interactions. Proc. Natl Acad. Sci. USA 115, e4304\u2013e4311 (2018).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"888_CR8","doi-asserted-by":"crossref","unstructured":"Zhong, Y. et al. Emerging machine learning techniques in predicting adverse drug reactions. In Machine Learning and Deep Learning in Computational Toxicology 53\u201382 (Springer, 2023).","DOI":"10.1007\/978-3-031-20730-3_3"},{"key":"888_CR9","doi-asserted-by":"publisher","first-page":"i457","DOI":"10.1093\/bioinformatics\/bty294","volume":"34","author":"M Zitnik","year":"2018","unstructured":"Zitnik, M., Agrawal, M. & Leskovec, J. Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics 34, i457\u2013i466 (2018).","journal-title":"Bioinformatics"},{"key":"888_CR10","doi-asserted-by":"crossref","unstructured":"Karim, M. R. et al. Drug-drug interaction prediction based on knowledge graph embeddings and convolutional-LSTM network. In Proc. 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics 113\u2013123 (ACM, 2019).","DOI":"10.1145\/3307339.3342161"},{"key":"888_CR11","doi-asserted-by":"crossref","unstructured":"Huang, K., Xiao, C., Hoang, T., Glass, L. & Sun, J. CASTER: predicting drug interactions with chemical substructure representation. In Proc. AAAI Conference on Artificial Intelligence 702\u2013709 (2020).","DOI":"10.1609\/aaai.v34i01.5412"},{"key":"888_CR12","doi-asserted-by":"crossref","unstructured":"Deng, Y. et al. META-DDIE: predicting drug-drug interaction events with few-shot learning. Brief. Bioinform. 23, bbab514 (2022).","DOI":"10.1093\/bib\/bbab514"},{"key":"888_CR13","doi-asserted-by":"crossref","unstructured":"Xu, N., Wang, P., Chen, L., Tao, J. & Zhao, J. MR-GNN: multi-resolution and dual graph neural network for predicting structured entity interactions. In Proc. 28th International Joint Conference on Artificial Intelligence 3968\u20133974 (AAAI Press, 2019).","DOI":"10.24963\/ijcai.2019\/551"},{"key":"888_CR14","doi-asserted-by":"crossref","unstructured":"Li, Z. et al. DSN-DDI: an accurate and generalized framework for drug-drug interaction prediction by dual-view representation learning. Brief. Bioinform. 24, bbac597 (2023).","DOI":"10.1093\/bib\/bbac597"},{"key":"888_CR15","doi-asserted-by":"crossref","unstructured":"Guo, Z. et al. Graph-based molecular representation learning. In Proc. Thirty-Second International Joint Conference on Artificial Intelligence 6638\u20136646 (2023).","DOI":"10.24963\/ijcai.2023\/744"},{"key":"888_CR16","doi-asserted-by":"publisher","first-page":"8749","DOI":"10.1021\/acs.jmedchem.9b00959","volume":"63","author":"Z Xiong","year":"2020","unstructured":"Xiong, Z. et al. Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism. J. Med. Chem. 63, 8749\u20138760 (2020).","journal-title":"J. Med. Chem."},{"key":"888_CR17","doi-asserted-by":"crossref","unstructured":"Zhang, X. C. et al. MG-BERT: leveraging unsupervised atomic representation learning for molecular property prediction. Brief. Bioinform. 22, bbab152 (2021).","DOI":"10.1093\/bib\/bbab152"},{"key":"888_CR18","unstructured":"Yu, Z. & Gao, H. Molecular representation learning via heterogeneous motif graph neural networks. In International Conference on Machine Learning 25581\u201325594 (PMLR, 2022)."},{"key":"888_CR19","unstructured":"Zhang, Z., Liu, Q., Wang, H., Lu, C. & Lee, C.-K. Motif-based graph self-supervised learning for molecular property prediction. In Proc. 35th International Conference on Neural Information Processing Systems 15870\u201315882 (Curran Associates, 2021)."},{"key":"888_CR20","doi-asserted-by":"publisher","first-page":"e0147606","DOI":"10.1371\/journal.pone.0147606","volume":"11","author":"HC Bucher","year":"2016","unstructured":"Bucher, H. C., Achermann, R., Stohler, N. & Meier, C. R. Surveillance of physicians causing potential drug-drug interactions in ambulatory care: a pilot study in Switzerland. PLoS ONE 11, e0147606 (2016).","journal-title":"PLoS ONE"},{"key":"888_CR21","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1517\/14740338.2011.583916","volume":"10","author":"PL Smithburger","year":"2011","unstructured":"Smithburger, P. L., Buckley, M. S., Bejian, S., Burenheide, K. & Kane-Gill, S. L. A critical evaluation of clinical decision support for the detection of drug-drug interactions. Expert Opin. Drug Saf. 10, 871\u2013882 (2011).","journal-title":"Expert Opin. Drug Saf."},{"key":"888_CR22","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1002\/cpt.1435","volume":"105","author":"A Tornio","year":"2019","unstructured":"Tornio, A., Filppula, A. M., Niemi, M. & Backman, J. T. Clinical studies on drug-drug interactions involving metabolism and transport: methodology, pitfalls, and interpretation. Clin. Pharmacol. Ther. 105, 1345\u20131361 (2019).","journal-title":"Clin. Pharmacol. Ther."},{"key":"888_CR23","doi-asserted-by":"crossref","unstructured":"Kaushik, S., Prasun, C. & Sharma, D. Translational and disease bioinformatics. In Encyclopedia of Bioinformatics and Computational Biology 1046\u20131057 (Elsevier, 2019).","DOI":"10.1016\/B978-0-12-809633-8.20302-6"},{"key":"888_CR24","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1038\/s41746-022-00639-0","volume":"5","author":"HY Jang","year":"2022","unstructured":"Jang, H. Y. et al. Machine learning-based quantitative prediction of drug exposure in drug-drug interactions using drug label information. npj Digit. Med. 5, 88 (2022).","journal-title":"npj Digit. Med."},{"key":"888_CR25","doi-asserted-by":"publisher","first-page":"3671","DOI":"10.1007\/s00204-020-02936-7","volume":"94","author":"J Hakkola","year":"2020","unstructured":"Hakkola, J., Hukkanen, J., Turpeinen, M. & Pelkonen, O. Inhibition and induction of CYP enzymes in humans: an update. Arch. Toxicol. 94, 3671\u20133722 (2020).","journal-title":"Arch. Toxicol."},{"key":"888_CR26","doi-asserted-by":"publisher","first-page":"846","DOI":"10.3390\/pharmaceutics12090846","volume":"12","author":"M Deodhar","year":"2020","unstructured":"Deodhar, M. et al. Mechanisms of CYP450 inhibition: understanding drug-drug interactions due to mechanism-based inhibition in clinical practice. Pharmaceutics 12, 846 (2020).","journal-title":"Pharmaceutics"},{"key":"888_CR27","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1109\/JBHI.2019.2932740","volume":"24","author":"N Liu","year":"2020","unstructured":"Liu, N., Chen, C. B. & Kumara, S. Semi-supervised learning algorithm for identifying high-priority drug-drug interactions through adverse event reports. IEEE J. Biomed. Health Inform. 24, 57\u201368 (2020).","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"888_CR28","doi-asserted-by":"publisher","first-page":"2112","DOI":"10.1016\/j.csbj.2022.04.021","volume":"20","author":"TH Vo","year":"2022","unstructured":"Vo, T. H., Nguyen, N. T. K., Kha, Q. H. & Le, N. Q. K. On the road to explainable AI in drug-drug interactions prediction: a systematic review. Comput. Struct. Biotechnol. J. 20, 2112\u20132123 (2022).","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"888_CR29","doi-asserted-by":"crossref","unstructured":"Wang, Y. et al. Identification of vital chemical information via visualization of graph neural networks. Brief. Bioinform. 24, bbac577 (2023).","DOI":"10.1093\/bib\/bbac577"},{"key":"888_CR30","doi-asserted-by":"publisher","first-page":"4896","DOI":"10.1021\/jm300065h","volume":"55","author":"ST Orr","year":"2012","unstructured":"Orr, S. T. et al. Mechanism-based inactivation (MBI) of cytochrome P450 enzymes: structure-activity relationships and discovery strategies to mitigate drug-drug interaction risks. J. Med. Chem. 55, 4896\u20134933 (2012).","journal-title":"J. Med. Chem."},{"key":"888_CR31","doi-asserted-by":"publisher","first-page":"368504211070183","DOI":"10.1177\/00368504211070183","volume":"105","author":"KD Georgiev","year":"2022","unstructured":"Georgiev, K. D., Hvarchanova, N., Stoychev, E. & Kanazirev, B. Prevalence of polypharmacy and risk of potential drug-drug interactions among hospitalized patients with emphasis on the pharmacokinetics. Sci. Prog. 105, 368504211070183 (2022).","journal-title":"Sci. Prog."},{"key":"888_CR32","doi-asserted-by":"publisher","first-page":"D1074","DOI":"10.1093\/nar\/gkx1037","volume":"46","author":"DS Wishart","year":"2018","unstructured":"Wishart, D. S. et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46, D1074\u2013D1082 (2018).","journal-title":"Nucleic Acids Res."},{"key":"888_CR33","doi-asserted-by":"publisher","first-page":"D237","DOI":"10.1093\/nar\/gkp970","volume":"38","author":"S Preissner","year":"2010","unstructured":"Preissner, S. et al. SuperCYP: a comprehensive database on cytochrome P450 enzymes including a tool for analysis of CYP-drug interactions. Nucleic Acids Res. 38, D237\u2013D243 (2010).","journal-title":"Nucleic Acids Res."},{"key":"888_CR34","doi-asserted-by":"publisher","first-page":"D1200","DOI":"10.1093\/nar\/gkab880","volume":"50","author":"G Xiong","year":"2022","unstructured":"Xiong, G. et al. DDInter: an online drug-drug interaction database towards improving clinical decision-making and patient safety. Nucleic Acids Res. 50, D1200\u2013D1207 (2022).","journal-title":"Nucleic Acids Res."},{"key":"888_CR35","unstructured":"Center for Drug Evaluation and Research. New Drug Therapy Approvals 2023 (US FDA, 2023)."},{"key":"888_CR36","doi-asserted-by":"publisher","first-page":"e177","DOI":"10.1016\/j.ddtec.2012.09.011","volume":"10","author":"A Kamel","year":"2013","unstructured":"Kamel, A. & Harriman, S. Inhibition of cytochrome P450 enzymes and biochemical aspects of mechanism-based inactivation (MBI). Drug Discov. Today Technol. 10, e177\u2013e189 (2013).","journal-title":"Drug Discov. Today Technol."},{"key":"888_CR37","doi-asserted-by":"publisher","first-page":"9866","DOI":"10.3390\/ijms23179866","volume":"23","author":"NHC Loos","year":"2022","unstructured":"Loos, N. H. C., Beijnen, J. H. & Schinkel, A. H. The mechanism-based inactivation of CYP3A4 by ritonavir: what mechanism? Int. J. Mol. Sci. 23, 9866 (2022).","journal-title":"Int. J. Mol. Sci."},{"key":"888_CR38","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1124\/mol.114.094862","volume":"86","author":"BM Rock","year":"2014","unstructured":"Rock, B. M., Hengel, S. M., Rock, D. A., Wienkers, L. C. & Kunze, K. L. Characterization of ritonavir-mediated inactivation of cytochrome P450 3A4. Mol. Pharmacol. 86, 665\u2013674 (2014).","journal-title":"Mol. Pharmacol."},{"key":"888_CR39","doi-asserted-by":"publisher","first-page":"1365","DOI":"10.1002\/cpdd.948","volume":"10","author":"Z Wang","year":"2021","unstructured":"Wang, Z. et al. Impact of paroxetine, a strong CYP2D6 inhibitor, on SPN-812 (viloxazine extended-release) pharmacokinetics in healthy adults. Clin. Pharmacol. Drug Dev. 10, 1365\u20131374 (2021).","journal-title":"Clin. Pharmacol. Drug Dev."},{"key":"888_CR40","doi-asserted-by":"crossref","unstructured":"Harbeson, S. L. & Tung, R. D. Deuterium in drug discovery and development. Annu. Rep. Med. Chem. 46, 403\u2013417 (2011).","DOI":"10.1016\/B978-0-12-386009-5.00003-5"},{"key":"888_CR41","doi-asserted-by":"publisher","first-page":"11158","DOI":"10.1021\/acs.jmedchem.8b01252","volume":"61","author":"Y Li","year":"2018","unstructured":"Li, Y. et al. Novel tetrazole-containing analogues of itraconazole as potent antiangiogenic agents with reduced cytochrome P450 3A4 inhibition. J. Med. Chem. 61, 11158\u201311168 (2018).","journal-title":"J. Med. Chem."},{"key":"888_CR42","doi-asserted-by":"publisher","first-page":"2256","DOI":"10.1074\/jbc.M008799200","volume":"276","author":"M Shou","year":"2001","unstructured":"Shou, M. et al. A kinetic model for the metabolic interaction of two substrates at the active site of cytochrome P450 3A4. J. Biol. Chem. 276, 2256\u20132262 (2001).","journal-title":"J. Biol. Chem."},{"key":"888_CR43","doi-asserted-by":"publisher","first-page":"e0149225","DOI":"10.1371\/journal.pone.0149225","volume":"11","author":"NM Midde","year":"2016","unstructured":"Midde, N. M. et al. Effect of ethanol on the metabolic characteristics of HIV-1 integrase inhibitor elvitegravir and elvitegravir\/cobicistat with CYP3A: an analysis using a newly developed LC-MS\/MS method. PLoS ONE 11, e0149225 (2016).","journal-title":"PLoS ONE"},{"key":"888_CR44","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1046\/j.1365-2125.2000.00271.x","volume":"50","author":"S Palovaara","year":"2000","unstructured":"Palovaara, S. et al. Effect of an oral contraceptive preparation containing ethinylestradiol and gestodene on CYP3A4 activity as measured by midazolam 1'-hydroxylation. Br. J. Clin. Pharmacol. 50, 333\u2013337 (2000).","journal-title":"Br. J. Clin. Pharmacol."},{"key":"888_CR45","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1016\/j.tips.2016.05.006","volume":"37","author":"FP Guengerich","year":"2016","unstructured":"Guengerich, F. P., Waterman, M. R. & Egli, M. Recent structural insights into cytochrome P450 function. Trends Pharmacol. Sci. 37, 625\u2013640 (2016).","journal-title":"Trends Pharmacol. Sci."},{"key":"888_CR46","doi-asserted-by":"publisher","first-page":"592","DOI":"10.3390\/pharmaceutics14030592","volume":"14","author":"P Bachmann","year":"2022","unstructured":"Bachmann, P. et al. Prevalence and severity of potential drug-drug interactions in patients with multiple sclerosis with and without polypharmacy. Pharmaceutics 14, 592 (2022).","journal-title":"Pharmaceutics"},{"key":"888_CR47","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-022-01783-z","volume":"22","author":"G Van De Sijpe","year":"2022","unstructured":"Van De Sijpe, G. et al. Overall performance of a drug-drug interaction clinical decision support system: quantitative evaluation and end-user survey. BMC Med. Inform. Decis. Mak. 22, 48 (2022).","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"888_CR48","doi-asserted-by":"publisher","first-page":"18141","DOI":"10.1039\/D0CP01474E","volume":"22","author":"SY Louis","year":"2020","unstructured":"Louis, S. Y. et al. Graph convolutional neural networks with global attention for improved materials property prediction. Phys. Chem. Chem. Phys. 22, 18141\u201318148 (2020).","journal-title":"Phys. Chem. Chem. Phys."},{"key":"888_CR49","doi-asserted-by":"publisher","first-page":"1503","DOI":"10.1002\/cmdc.200800178","volume":"3","author":"J Degen","year":"2008","unstructured":"Degen, J., Wegscheid-Gerlach, C., Zaliani, A. & Rarey, M. On the art of compiling and using \u2018drug-like\u2019 chemical fragment spaces. ChemMedChem 3, 1503\u20131507 (2008).","journal-title":"ChemMedChem"},{"key":"888_CR50","doi-asserted-by":"publisher","first-page":"D1100","DOI":"10.1093\/nar\/gkr777","volume":"40","author":"A Gaulton","year":"2012","unstructured":"Gaulton, A. et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 40, D1100\u2013D1107 (2012).","journal-title":"Nucleic Acids Res."},{"key":"888_CR51","doi-asserted-by":"crossref","unstructured":"Han, S. et al. HimGNN: a novel hierarchical molecular graph representation learning framework for property prediction. Brief. Bioinform. 24, bbad305 (2023).","DOI":"10.1093\/bib\/bbad305"},{"key":"888_CR52","unstructured":"Vaswani, A. et al. Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)."},{"key":"888_CR53","unstructured":"Wu, Z., Liu, Z., Lin, J., Lin, Y. & Han, S. Lite transformer with long-short range attention. In International Conference on Learning Representations (2020)."},{"key":"888_CR54","unstructured":"Dwivedi, V. P. & Bresson, X. A generalization of transformer networks to graphs. In AAAI Workshop on Deep Learning on Graphs: Methods and Applications (DLG-AAAI, 2021)."},{"key":"888_CR55","doi-asserted-by":"crossref","unstructured":"Wu, C., Wu, F. & Huang, Y. DA-Transformer: distance-aware transformer. In Proc. 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies 2059\u20132068 (NAACL 2021).","DOI":"10.18653\/v1\/2021.naacl-main.166"},{"key":"888_CR56","unstructured":"Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. BERT: pre-training of deep bidirectional transformers for language understanding. In Proc. 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies 4171\u20134186 (NAACL 2019)."},{"key":"888_CR57","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2023.102640","volume":"144","author":"Y Zhong","year":"2023","unstructured":"Zhong, Y. et al. DDI-GCN: drug-drug interaction prediction via explainable graph convolutional networks. Artif. Intell. Med. 144, 102640 (2023).","journal-title":"Artif. Intell. Med."},{"key":"888_CR58","doi-asserted-by":"publisher","first-page":"18209","DOI":"10.1021\/acs.jmedchem.1c01830","volume":"64","author":"D Jiang","year":"2021","unstructured":"Jiang, D. et al. InteractionGraphNet: a novel and efficient deep graph representation learning framework for accurate protein-ligand interaction predictions. J. Med. Chem. 64, 18209\u201318232 (2021).","journal-title":"J. Med. Chem."},{"key":"888_CR59","doi-asserted-by":"crossref","unstructured":"Abadi, M. TensorFlow: learning functions at scale. In Proc. 21st ACM SIGPLAN International Conference on Functional Programming 1 (ACM, 2016).","DOI":"10.1145\/2951913.2976746"},{"key":"888_CR60","first-page":"5281","volume":"8","author":"G Landrum","year":"2013","unstructured":"Landrum, G. RDKit: a software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 5281 (2013).","journal-title":"Greg Landrum"},{"key":"888_CR61","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011).","journal-title":"J. Mach. Learn. Res."},{"key":"888_CR62","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","volume":"585","author":"CR Harris","year":"2020","unstructured":"Harris, C. R. et al. Array programming with NumPy. Nature 585, 357\u2013362 (2020).","journal-title":"Nature"},{"key":"888_CR63","first-page":"1","volume":"14","author":"W McKinney","year":"2011","unstructured":"McKinney, W. pandas: a foundational Python library for data analysis and statistics. Python High Perf. Sci. Comput. 14, 1\u20139 (2011).","journal-title":"Python High Perf. Sci. Comput."},{"key":"888_CR64","doi-asserted-by":"crossref","unstructured":"Kwon, S. & Yoon, S. DeepCCI: end-to-end deep learning for chemical-chemical interaction prediction. In Proc. 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics 203\u2013212 (ACM, 2017).","DOI":"10.1145\/3107411.3107451"},{"key":"888_CR65","doi-asserted-by":"publisher","first-page":"830","DOI":"10.1093\/bioinformatics\/btaa880","volume":"37","author":"K Huang","year":"2021","unstructured":"Huang, K., Xiao, C., Glass, L. M. & Sun, J. MolTrans: molecular interaction transformer for drug\u2013target interaction prediction. Bioinformatics 37, 830\u2013836 (2021).","journal-title":"Bioinformatics"},{"key":"888_CR66","doi-asserted-by":"crossref","unstructured":"Pathak, Y., Laghuvarapu, S., Mehta, S. & Priyakumar, U. D. Chemically Interpretable Graph Interaction Network for prediction of pharmacokinetic properties of drug-like molecules. In Proc. AAAI Conference on Artificial Intelligence 873\u2013880 (2020).","DOI":"10.1609\/aaai.v34i01.5433"},{"key":"888_CR67","unstructured":"Lee, N. et al. Conditional Graph Information Bottleneck for molecular relational learning. In International Conference on Machine Learning 18852\u201318871 (PMLR, 2023)."},{"key":"888_CR68","doi-asserted-by":"publisher","unstructured":"Zhong, Y., Li, G.,Yang, J., Zheng, H., Yu, Y., Zhang, J., Luo, H., Wang, B. & Weng, Z. Learning motif-based graph for drug-drug interaction prediction via local-global self-attention. Code Ocean https:\/\/doi.org\/10.24433\/CO.0704680.v1 (2024).","DOI":"10.24433\/CO.0704680.v1"},{"key":"888_CR69","unstructured":"Center for Drug Evaluation and Research. Clinical Drug Interaction Studies\u2014Cytochrome P450 Enzyme- and Transporter-Mediated Drug Interactions Guidance for Industry (US FDA, 2020)."}],"container-title":["Nature Machine Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s42256-024-00888-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42256-024-00888-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42256-024-00888-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T18:03:31Z","timestamp":1726855411000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s42256-024-00888-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,27]]},"references-count":69,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["888"],"URL":"https:\/\/doi.org\/10.1038\/s42256-024-00888-6","relation":{},"ISSN":["2522-5839"],"issn-type":[{"value":"2522-5839","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,27]]},"assertion":[{"value":"13 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 August 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}