{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T02:56:58Z","timestamp":1782961018960,"version":"3.54.5"},"reference-count":69,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Science and Technology Innovation 2030-New Generation Artificial Intelligence Major Project","award":["2018AAA0100103"],"award-info":[{"award-number":["2018AAA0100103"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62002297"],"award-info":[{"award-number":["62002297"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61722212"],"award-info":[{"award-number":["61722212"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072378"],"award-info":[{"award-number":["62072378"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172338"],"award-info":[{"award-number":["62172338"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Neural Science Foundation of Shanxi Province","award":["2022JQ-700"],"award-info":[{"award-number":["2022JQ-700"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Drug\u2013drug interactions (DDIs) prediction is a challenging task in drug development and clinical application. Due to the extremely large complete set of all possible DDIs, computer-aided DDIs prediction methods are getting lots of attention in the pharmaceutical industry and academia. However, most existing computational methods only use single perspective information and few of them conduct the task based on the biomedical knowledge graph (BKG), which can provide more detailed and comprehensive drug lateral side information flow. To this end, a deep learning framework, namely DeepLGF, is proposed to fully exploit BKG fusing local\u2013global information to improve the performance of DDIs prediction. More specifically, DeepLGF first obtains chemical local information on drug sequence semantics through a natural language processing algorithm. Then a model of BFGNN based on graph neural network is proposed to extract biological local information on drug through learning embedding vector from different biological functional spaces. The global feature information is extracted from the BKG by our knowledge graph embedding method. In DeepLGF, for fusing local\u2013global features well, we designed four aggregating methods to explore the most suitable ones. Finally, the advanced fusing feature vectors are fed into deep neural network to train and predict. To evaluate the prediction performance of DeepLGF, we tested our method in three prediction tasks and compared it with state-of-the-art models. In addition, case studies of three cancer-related and COVID-19-related drugs further demonstrated DeepLGF\u2019s superior ability for potential DDIs prediction. The webserver of the DeepLGF predictor is freely available at http:\/\/120.77.11.78\/DeepLGF\/.<\/jats:p>","DOI":"10.1093\/bib\/bbac363","type":"journal-article","created":{"date-parts":[[2022,9,7]],"date-time":"2022-09-07T21:58:32Z","timestamp":1662587912000},"source":"Crossref","is-referenced-by-count":63,"title":["A biomedical knowledge graph-based method for drug\u2013drug interactions prediction through combining local and global features with deep neural networks"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1614-8988","authenticated-orcid":false,"given":"Zhong-Hao","family":"Ren","sequence":"first","affiliation":[{"name":"School of Information Engineering, Xijing University , Xi\u2019an 710100, China"},{"name":"School of Computer Science, Northwestern Polytechnical University , Xi\u2019an 710129, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1266-2696","authenticated-orcid":false,"given":"Zhu-Hong","family":"You","sequence":"additional","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University , Xi\u2019an 710129, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang-Qing","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Xijing University , Xi\u2019an 710100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li-Ping","family":"Li","sequence":"additional","affiliation":[{"name":"College of Grassland and Environment Sciences, Xinjiang Agricultural University , Urumqi 830052, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong-Jian","family":"Guan","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Xijing University , Xi\u2019an 710100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu-Xiang","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Xijing University , Xi\u2019an 710100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Xijing University , Xi\u2019an 710100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,9,6]]},"reference":[{"key":"2022092013240510800_ref1","first-page":"629","article-title":"Drug\u2013drug interaction studies: regulatory guidance and an industry perspective","volume-title":"AAPS J","author":"Prueksaritanont","year":"2013"},{"key":"2022092013240510800_ref2","first-page":"178","article-title":"Informatics confronts drug\u2013drug interactions","volume-title":"Trends Pharmacol Sci","author":"Percha","year":"2013"},{"key":"2022092013240510800_ref3","first-page":"227","article-title":"How far should we go? Perspective of drug-drug interaction studies in drug development","volume-title":"Drug Metab Pharmacokinet","author":"Kusuhara","year":"2014"},{"key":"2022092013240510800_ref4","first-page":"1","article-title":"A Comprehensive Review of Computational Methods for Drug-drug Interaction Detection","volume":"19","author":"Qiu","year":"2021","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2022092013240510800_ref5","first-page":"1421","article-title":"Keynote review: in vitro safety pharmacology profiling: an essential tool for successful drug development","volume-title":"Drug Discov Today","author":"Whitebread","year":"2005"},{"key":"2022092013240510800_ref6","doi-asserted-by":"crossref","article-title":"STNN-DDI: a substructure-aware tensor neural network to predict drug-drug interactions","author":"Yu","DOI":"10.1093\/bib\/bbac209"},{"key":"2022092013240510800_ref7","first-page":"2921","volume-title":"Multi-view graph contrastive representation learning for drug-drug interaction prediction","author":"Wang","year":"2021"},{"key":"2022092013240510800_ref8","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab133","article-title":"Drug\u2013drug interaction prediction with learnable size-adaptive molecular substructures","volume":"22","author":"Nyamabo","year":"2021","journal-title":"Brief Bioinform"},{"key":"2022092013240510800_ref9","article-title":"Predicting drug\u2013drug interactions by graph convolutional network with multi-kernel","volume":"23","author":"Wang","year":"2021","journal-title":"Brief Bioinform"},{"key":"2022092013240510800_ref10","article-title":"META-DDIE: predicting drug\u2013drug interaction events with few-shot learning","volume":"23","author":"Deng","year":"2021","journal-title":"Brief Bioinform"},{"key":"2022092013240510800_ref11","volume-title":"Proceedings of the Workshop on Scientific Document Understanding co-located with 35th AAAI Conference on Artificial Inteligence (AAAI)","author":"Mondal","year":"2021"},{"key":"2022092013240510800_ref12","first-page":"101","article-title":"Predicting and understanding comprehensive drug-drug interactions via semi-nonnegative matrix factorization","volume-title":"BMC Syst Biol","author":"Yu","year":"2018"},{"key":"2022092013240510800_ref13","first-page":"1","article-title":"Detecting drug communities and predicting comprehensive drug\u2013drug interactions via balance regularized semi-nonnegative matrix factorization","volume-title":"J Cheminform","author":"Shi","year":"2019"},{"key":"2022092013240510800_ref14","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbi.2020.103451","article-title":"Extracting drug-drug interactions from texts with BioBERT and multiple entity-aware attentions","volume-title":"J Biomed Inform","author":"Zhu","year":"2020"},{"key":"2022092013240510800_ref15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-016-1415-9","article-title":"Predicting potential drug-drug interactions by integrating chemical, biological phenotypic and network data","volume":"18","author":"Zhang","year":"2017","journal-title":"BMC Bioinformatics"},{"key":"2022092013240510800_ref16","first-page":"189","article-title":"SFLLN: a sparse feature learning ensemble method with linear neighborhood regularization for predicting drug\u2013drug interactions","volume-title":"Inf Sci","author":"Zhang","year":"2019"},{"key":"2022092013240510800_ref17","first-page":"1","article-title":"BioHackathon series in 2011 and 2012: penetration of ontology and linked data in life science domains","volume-title":"J Biomed Semantics","author":"Katayama","year":"2014"},{"key":"2022092013240510800_ref18","first-page":"2863","volume-title":"Canonical tensor decomposition for knowledge base completion","author":"Lacroix","year":"2018"},{"key":"2022092013240510800_ref19","volume-title":"Proceedings of the Thirty-second AAAI Conference on Artificial intelligence","author":"Dettmers","year":"2018"},{"key":"2022092013240510800_ref20","first-page":"bbaa256","article-title":"Drug\u2013drug interaction prediction with Wasserstein Adversarial Autoencoder-based knowledge graph embeddings","volume-title":"Brief Bioinform","author":"Dai","year":"2021"},{"key":"2022092013240510800_ref21","first-page":"2988","article-title":"SumGNN: multi-typed drug interaction prediction via efficient knowledge graph summarization","volume-title":"Bioinformatics","author":"Yu","year":"2020"},{"key":"2022092013240510800_ref22","first-page":"1","article-title":"Enhancing drug-drug interaction prediction using deep attention neural networks","author":"Liu","year":"2022","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2022092013240510800_ref23","first-page":"592","article-title":"INDI: a computational framework for inferring drug interactions and their associated recommendations","volume-title":"Mol Syst Biol","author":"Gottlieb","year":"2012"},{"key":"2022092013240510800_ref24","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0140816","article-title":"Predicting pharmacodynamic drug-drug interactions through signaling propagation interference on protein-protein interaction networks","volume-title":"PloS one","author":"Park","year":"2015"},{"key":"2022092013240510800_ref25","first-page":"1679","article-title":"Biological applications of knowledge graph embedding models","volume-title":"Brief Bioinform","author":"Mohamed","year":"2021"},{"key":"2022092013240510800_ref26","first-page":"1188","volume-title":"Distributed representations of sentences and documents","author":"Le","year":"2014"},{"key":"2022092013240510800_ref27","first-page":"2071","volume-title":"Complex embeddings for simple link prediction","author":"Trouillon","year":"2016"},{"key":"2022092013240510800_ref28","first-page":"D1035","article-title":"DrugBank 3.0: a comprehensive resource for \u2018omics\u2019 research on drugs","volume-title":"Nucleic Acids Res","author":"Knox","year":"2010"},{"key":"2022092013240510800_ref29","first-page":"D1091","article-title":"DrugBank 4.0: shedding new light on drug metabolism","volume-title":"Nucleic Acids Res","author":"Law","year":"2014"},{"key":"2022092013240510800_ref30","first-page":"D1074","article-title":"DrugBank 5.0: a major update to the DrugBank database for 2018","volume-title":"Nucleic Acids Res","author":"Wishart","year":"2018"},{"key":"2022092013240510800_ref31","first-page":"D901","article-title":"DrugBank: a knowledgebase for drugs, drug actions and drug targets","volume-title":"Nucleic Acids Res","author":"Wishart","year":"2008"},{"key":"2022092013240510800_ref32","first-page":"D668","article-title":"DrugBank: a comprehensive resource for in silico drug discovery and exploration","volume-title":"Nucleic Acids Res","author":"Wishart","year":"2006"},{"key":"2022092013240510800_ref33","article-title":"Drkg-drug repurposing knowledge graph for covid-19","volume-title":"arXiv preprint arXiv: 2010.09600","author":"Ioannidis","year":"2020"},{"key":"2022092013240510800_ref34","first-page":"1241","article-title":"Graph embedding on biomedical networks: methods, applications and evaluations","volume-title":"Bioinformatics","author":"Yue","year":"2020"},{"key":"2022092013240510800_ref35","article-title":"A simple but tough-to-beat baseline for sentence embeddings","volume-title":"ICLR","author":"Arora","year":"2016"},{"key":"2022092013240510800_ref36","article-title":"Efficient estimation of word representations in vector space","volume-title":"arXiv preprint arXiv:1301.3781","author":"Mikolov","year":"2013"},{"key":"2022092013240510800_ref37","first-page":"3111","volume-title":"Distributed representations of words and phrases and their compositionality","author":"Mikolov","year":"2013"},{"key":"2022092013240510800_ref38","first-page":"1338","article-title":"The EBI RDF platform: linked open data for the life sciences","volume-title":"Bioinformatics","author":"Jupp","year":"2014"},{"key":"2022092013240510800_ref39","first-page":"1","article-title":"Relations in biomedical ontologies","volume-title":"Genome Biol","author":"Smith","year":"2005"},{"key":"2022092013240510800_ref40","first-page":"2651","article-title":"MUFFIN: multi-scale feature fusion for drug\u2013drug interaction prediction","volume-title":"Bioinformatics","author":"Chen","year":"2021"},{"key":"2022092013240510800_ref41","first-page":"104","article-title":"NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug\u2013target interactions","volume-title":"Bioinformatics","author":"Wan","year":"2019"},{"key":"2022092013240510800_ref42","first-page":"5998","volume-title":"Attention is all you need","author":"Vaswani","year":"2017"},{"key":"2022092013240510800_ref43","first-page":"1","article-title":"DPDDI: a deep predictor for drug-drug interactions","volume-title":"BMC Bioinformatics","author":"Feng","year":"2020"},{"key":"2022092013240510800_ref44","first-page":"275","article-title":"Prediction of protein structural classes","volume-title":"Crit Rev Biochem Mol Biol","author":"Chou","year":"1995"},{"key":"2022092013240510800_ref45","first-page":"1","article-title":"Drug-drug interaction predicting by neural network using integrated similarity","volume-title":"Sci Rep","author":"Rohani","year":"2019"},{"key":"2022092013240510800_ref46","first-page":"1066","article-title":"Drug\u2014drug interaction through molecular structure similarity analysis","volume-title":"Am Med Inform Assoc","author":"Vilar","year":"2012"},{"key":"2022092013240510800_ref47","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0058321","article-title":"Detection of drug-drug interactions by modeling interaction profile fingerprints","volume-title":"PloS one","author":"Vilar","year":"2013"},{"key":"2022092013240510800_ref48","first-page":"1","article-title":"Label propagation prediction of drug-drug interactions based on clinical side effects","volume-title":"Sci Rep","author":"Zhang","year":"2015"},{"key":"2022092013240510800_ref49","first-page":"1","article-title":"Iscmf: integrated similarity-constrained matrix factorization for drug\u2013drug interaction prediction","volume":"9","author":"Rohani","year":"2020","journal-title":"NetwModel Anal Health Informatics Bioinformatics"},{"key":"2022092013240510800_ref50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-021-04325-y","article-title":"AttentionDDI: siamese attention-based deep learning method for drug\u2013drug interaction predictions","volume":"22","author":"Schwarz","year":"2021","journal-title":"BMC bioinformatics"},{"key":"2022092013240510800_ref51","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.ymeth.2020.05.014","article-title":"GCN-BMP: investigating graph representation learning for DDI prediction task","volume":"179","author":"Chen","year":"2020","journal-title":"Methods"},{"key":"2022092013240510800_ref52","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2021.20603","article-title":"Efficacy and safety of cannabidiol plus standard care vs standard care alone for the treatment of emotional exhaustion and burnout among frontline health care workers during the COVID-19 pandemic: a randomized clinical trial","volume":"4","author":"Crippa","year":"2021","journal-title":"JAMA Netw Open"},{"key":"2022092013240510800_ref53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13063-020-04643-1","article-title":"Efficacy of dexamethasone treatment for patients with the acute respiratory distress syndrome caused by COVID-19: study protocol for a randomized controlled superiority trial","volume":"21","author":"Villar","year":"2020","journal-title":"Trials"},{"key":"2022092013240510800_ref54","doi-asserted-by":"crossref","DOI":"10.1093\/bfgp\/elac004","article-title":"BioDKG\u2013DDI: predicting drug\u2013drug interactions based on drug knowledge graph fusing biochemical information","author":"Ren","year":"2022","journal-title":"Brief Funct Genomics"},{"key":"2022092013240510800_ref55","first-page":"1","article-title":"Biomedical Knowledge Graph Embedding with Capsule Network for Multi-label Drug-Drug Interaction Prediction","author":"Su","year":"2022","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2022092013240510800_ref56","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbac140","article-title":"Attention-based Knowledge Graph Representation Learning for Predicting Drug-drug Interactions","volume":"23","author":"Su","year":"2022","journal-title":"Brief Bioinform"},{"key":"2022092013240510800_ref57","doi-asserted-by":"crossref","first-page":"758","DOI":"10.3390\/biology11050758","article-title":"BioChemDDI: predicting drug-drug interactions by fusing biochemical and structural information through a self-attention mechanism","volume":"11","author":"Ren","year":"2022","journal-title":"Biology"},{"key":"2022092013240510800_ref58","doi-asserted-by":"crossref","DOI":"10.7554\/eLife.26726","article-title":"Systematic integration of biomedical knowledge prioritizes drugs for repurposing","volume":"6","author":"Himmelstein","year":"2017","journal-title":"Elife"},{"key":"2022092013240510800_ref59","article-title":"Translating embeddings for modeling multi-relational data","volume":"26","author":"Bordes","year":"2013","journal-title":"Adv Neural Inf Process Syste"},{"key":"2022092013240510800_ref60","first-page":"891","volume-title":"Proceedings of the Proceedings of the 24th ACM International on Conference on Information and Knowledge Management","author":"Cao","year":"2015"},{"key":"2022092013240510800_ref61","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1145\/2736277.2741093","volume-title":"Proceedings of the 24th International Conference on World Wide Web","author":"Tang","year":"2015"},{"key":"2022092013240510800_ref62","first-page":"1225","volume-title":"Proceedings of the Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Wang","year":"2016"},{"key":"2022092013240510800_ref63","first-page":"603","volume-title":"TeX-Graph: coupled tensor-matrix knowledge-graph embedding for COVID-19 drug repurposing","author":"Kanatsoulis","year":"2021"},{"key":"2022092013240510800_ref64","first-page":"100036","article-title":"Understanding the performance of knowledge graph embeddings in drug discovery","volume":"2","author":"Bonner","year":"2022","journal-title":"Artif Intell Life Sci"},{"key":"2022092013240510800_ref65","doi-asserted-by":"crossref","first-page":"i60","DOI":"10.1093\/bioinformatics\/btu269","article-title":"Inductive matrix completion for predicting gene\u2013disease associations","volume":"30","author":"Natarajan","year":"2014","journal-title":"Bioinformatics"},{"key":"2022092013240510800_ref66","first-page":"2739","article-title":"KGNN: knowledge graph neural network for drug-drug interaction prediction","volume":"380","author":"Lin","year":"2020","journal-title":"IJCAI"},{"key":"2022092013240510800_ref67","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1109\/JPROC.2015.2483592","article-title":"A review of relational machine learning for knowledge graphs","volume":"104","author":"Nickel","year":"2015","journal-title":"Proc IEEE"},{"key":"2022092013240510800_ref68","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.knosys.2018.10.008","article-title":"Knowledge graph embedding with concepts","volume":"164","author":"Guan","year":"2019","journal-title":"Knowledge-Based Systems"},{"key":"2022092013240510800_ref69","author":"Rozemberczki","journal-title":"A unified view of relational deep learning for drug pair scoring"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/5\/bbac363\/45939173\/bbac363.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/5\/bbac363\/45939173\/bbac363.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,20]],"date-time":"2022-09-20T18:15:45Z","timestamp":1663697745000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbac363\/6692550"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9]]},"references-count":69,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,9,20]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbac363","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,9]]},"published":{"date-parts":[[2022,9]]},"article-number":"bbac363"}}