{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T17:03:24Z","timestamp":1776272604063,"version":"3.50.1"},"reference-count":43,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072206"],"award-info":[{"award-number":["62072206"]}],"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":["61772381"],"award-info":[{"award-number":["61772381"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Huazhong Agricultural University Scientific & Technological Self-innovation Foundation"},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2662021JC008"],"award-info":[{"award-number":["2662021JC008"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Predicting the response of a cancer cell line to a therapeutic drug is an important topic in modern oncology that can help personalized treatment for cancers. Although numerous machine learning methods have been developed for cancer drug response (CDR) prediction, integrating diverse information about cancer cell lines, drugs and their known responses still remains a great challenge. In this paper, we propose a graph neural network method with contrastive learning for CDR prediction. GraphCDR constructs a graph neural network based on multi-omics profiles of cancer cell lines, the chemical structure of drugs and known cancer cell line-drug responses for CDR prediction, while a contrastive learning task is presented as a regularizer within a multi-task learning paradigm to enhance the generalization ability. In the computational experiments, GraphCDR outperforms state-of-the-art methods under different experimental configurations, and the ablation study reveals the key components of GraphCDR: biological features, known cancer cell line-drug responses and contrastive learning are important for the high-accuracy CDR prediction. The experimental analyses imply the predictive power of GraphCDR and its potential value in guiding anti-cancer drug selection.<\/jats:p>","DOI":"10.1093\/bib\/bbab457","type":"journal-article","created":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T11:38:41Z","timestamp":1633693121000},"source":"Crossref","is-referenced-by-count":94,"title":["GraphCDR: a graph neural network method with contrastive learning for cancer drug response prediction"],"prefix":"10.1093","volume":"23","author":[{"given":"Xuan","family":"Liu","sequence":"first","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China"}]},{"given":"Congzhi","family":"Song","sequence":"additional","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China"}]},{"given":"Feng","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China"}]},{"given":"Haitao","family":"Fu","sequence":"additional","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China"}]},{"given":"Wenjie","family":"Xiao","sequence":"additional","affiliation":[{"name":"Information School, University of Washington, Washington, 98105, USA"}]},{"given":"Wen","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China"}]}],"member":"286","published-online":{"date-parts":[[2021,11,1]]},"reference":[{"issue":"1","key":"2022012000285089500_ref1","doi-asserted-by":"crossref","first-page":"3","DOI":"10.2174\/1389450120666190923162203","article-title":"Bioinformatics approaches for anti-cancer drug discovery","volume":"21","author":"Li","year":"2020","journal-title":"Curr Drug Targets"},{"issue":"7391","key":"2022012000285089500_ref2","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1038\/nature11003","article-title":"The cancer cell line Encyclopedia enables predictive modelling of anticancer drug sensitivity","volume":"483","author":"Barretina","year":"2012","journal-title":"Nature"},{"issue":"3","key":"2022012000285089500_ref3","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1016\/j.cell.2016.06.017","article-title":"A landscape of pharmacogenomic interactions in cancer","volume":"166","author":"Iorio","year":"2016","journal-title":"Cell"},{"issue":"17","key":"2022012000285089500_ref4","doi-asserted-by":"crossref","first-page":"i455","DOI":"10.1093\/bioinformatics\/btw433","article-title":"Drug response prediction by inferring pathway-response associations with kernelized Bayesian matrix factorization","volume":"32","author":"Ammad-Ud-Din","year":"2016","journal-title":"Bioinformatics"},{"issue":"22","key":"2022012000285089500_ref5","doi-asserted-by":"crossref","first-page":"3907","DOI":"10.1093\/bioinformatics\/bty452","article-title":"Predicting cancer drug response using a recommender system","volume":"34","author":"Suphavilai","year":"2018","journal-title":"Bioinformatics"},{"issue":"1","key":"2022012000285089500_ref6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12885-017-3500-5","article-title":"Improved anticancer drug response prediction in cell lines using matrix factorization with similarity regularization","volume":"17","author":"Wang","year":"2017","journal-title":"BMC Cancer"},{"issue":"1","key":"2022012000285089500_ref7","first-page":"1","article-title":"Drug response prediction as a link prediction problem","volume":"7","author":"Stanfield","year":"2017","journal-title":"Sci Rep"},{"issue":"5","key":"2022012000285089500_ref8","first-page":"1","article-title":"A link prediction approach to cancer drug sensitivity prediction","volume":"11","author":"Turki","year":"2017","journal-title":"BMC Syst Biol"},{"issue":"9","key":"2022012000285089500_ref9","doi-asserted-by":"crossref","first-page":"e1004498","DOI":"10.1371\/journal.pcbi.1004498","article-title":"Predicting anticancer drug responses using a dual-layer integrated cell line-drug network model","volume":"11","author":"Zhang","year":"2015","journal-title":"PLoS Comput Biol"},{"issue":"1","key":"2022012000285089500_ref10","first-page":"1","article-title":"A novel heterogeneous network-based method for drug response prediction in cancer cell lines","volume":"8","author":"Zhang","year":"2018","journal-title":"Sci Rep"},{"key":"2022012000285089500_ref11","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btab466","article-title":"Predicting anti-cancer drug response by finding optimal subset of drugs","author":"Meybodi","year":"2021","journal-title":"Bioinformatics"},{"issue":"9","key":"2022012000285089500_ref12","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1093\/bioinformatics\/bty848","article-title":"A novel approach for drug response prediction in cancer cell lines via network representation learning","volume":"35","author":"Yang","year":"2019","journal-title":"Bioinformatics"},{"issue":"7","key":"2022012000285089500_ref13","doi-asserted-by":"crossref","first-page":"e0219774","DOI":"10.1371\/journal.pone.0219774","article-title":"Predicting drug activity against cancer cells by random forest models based on minimal genomic information and chemical properties","volume":"14","author":"Lind","year":"2019","journal-title":"PLoS One"},{"key":"2022012000285089500_ref14","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.ymeth.2019.02.009","article-title":"Deep-Resp-Forest: a deep forest model to predict anti-cancer drug response","volume":"166","author":"Su","year":"2019","journal-title":"Methods"},{"key":"2022012000285089500_ref15","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymeth.2020.08.006","article-title":"Prediction of drug response in multilayer networks based on fusion of multiomics data","volume":"192","author":"Yu","year":"2021","journal-title":"Methods"},{"issue":"1","key":"2022012000285089500_ref16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-021-22170-8","article-title":"Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs","volume":"12","author":"Gerdes","year":"2021","journal-title":"Nat Commun"},{"key":"2022012000285089500_ref17","doi-asserted-by":"crossref","DOI":"10.1109\/TCBB.2019.2919581","article-title":"DeepDSC: a deep learning method to predict drug sensitivity of cancer cell lines","volume":"18","author":"Li","year":"2021","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"1","key":"2022012000285089500_ref18","first-page":"1","article-title":"RefDNN: a reference drug based neural network for more accurate prediction of anticancer drug resistance","volume":"10","author":"Choi","year":"2020","journal-title":"Sci Rep"},{"issue":"1","key":"2022012000285089500_ref19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-019-2910-6","article-title":"Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network","volume":"20","author":"Liu","year":"2019","journal-title":"BMC Bioinformatics"},{"issue":"Supplement_2","key":"2022012000285089500_ref20","doi-asserted-by":"crossref","first-page":"i911","DOI":"10.1093\/bioinformatics\/btaa822","article-title":"DeepCDR: a hybrid graph convolutional network for predicting cancer drug response","volume":"36","author":"Liu","year":"2020","journal-title":"Bioinformatics"},{"key":"2022012000285089500_ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CCWC47524.2020.9031163","volume-title":"Annual Computing and Communication Workshop and Conference (CCWC)","author":"Li","year":"2020"},{"key":"2022012000285089500_ref22","article-title":"How powerful are graph neural networks?","volume-title":"International Conference on Learning Representations (ICLR).","author":"Xu","year":"2019"},{"issue":"8","key":"2022012000285089500_ref23","doi-asserted-by":"crossref","first-page":"2538","DOI":"10.1093\/bioinformatics\/btz965","article-title":"Neural inductive matrix completion with graph convolutional networks for miRNA-disease association prediction","volume":"36","author":"Li","year":"2020","journal-title":"Bioinformatics"},{"key":"2022012000285089500_ref24","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaa243","article-title":"Predicting drug\u2013disease associations through layer attention graph convolutional network","volume":"22","author":"Yu","year":"2021","journal-title":"Briefings in Bioinformatics"},{"key":"2022012000285089500_ref25","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab133","article-title":"SSI\u2013DDI: substructure\u2013substructure interactions for drug\u2013drug interaction prediction","author":"Nyamabo","year":"2021","journal-title":"Briefings in Bioinformatics"},{"key":"2022012000285089500_ref26","volume-title":"nternational Conference on Learning Representations (ICLR)","author":"Velickovic","year":"2019"},{"key":"2022012000285089500_ref27","first-page":"1597","volume-title":"International Conference on Machine Learning (ICML)","author":"Chen","year":"2020"},{"key":"2022012000285089500_ref28","doi-asserted-by":"publisher","first-page":"1150","DOI":"10.1145\/3394486.3403168","volume-title":"International Conference on Knowledge Discovery & Data Mining (KDD)","author":"Qiu","year":"2020"},{"issue":"8","key":"2022012000285089500_ref29","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1007\/s10822-016-9938-8","article-title":"Molecular graph convolutions: moving beyond fingerprints","volume":"30","author":"Kearnes","year":"2016","journal-title":"J Comput Aided Mol Des"},{"issue":"14","key":"2022012000285089500_ref30","doi-asserted-by":"crossref","first-page":"i501","DOI":"10.1093\/bioinformatics\/btz318","article-title":"MOLI: multi-omics late integration with deep neural networks for drug response prediction","volume":"35","author":"Sharifi-Noghabi","year":"2019","journal-title":"Bioinformatics"},{"issue":"11","key":"2022012000285089500_ref31","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1038\/s41568-018-0060-1","article-title":"The COSMIC cancer gene census: describing genetic dysfunction across all human cancers","volume":"18","author":"Sondka","year":"2018","journal-title":"Nat Rev Cancer"},{"issue":"D1","key":"2022012000285089500_ref32","doi-asserted-by":"crossref","first-page":"D1102","DOI":"10.1093\/nar\/gky1033","article-title":"PubChem 2019 update: improved access to chemical data","volume":"47","author":"Kim","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2022012000285089500_ref33","article-title":"Convolutional networks on graphs for learning molecular fingerprints","volume-title":"Conference on Neural Information Processing Systems (NeurIPS)","author":"Duvenaud","year":"2015"},{"key":"2022012000285089500_ref34","volume-title":"Deep learning for the life sciences: applying deep learning to genomics, microscopy, drug discovery, and more","author":"Ramsundar","year":"2019"},{"issue":"1","key":"2022012000285089500_ref35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12885-015-1492-6","article-title":"Anticancer drug sensitivity prediction in cell lines from baseline gene expression through recursive feature selection","volume":"15","author":"Dong","year":"2015","journal-title":"BMC Cancer"},{"key":"2022012000285089500_ref36","first-page":"1026","volume-title":"International conference on computer vision (ICCV)","author":"He","year":"2015"},{"issue":"1","key":"2022012000285089500_ref37","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.canlet.2012.01.039","article-title":"Dasatinib synergizes with both cytotoxic and signal transduction inhibitors in heterogeneous breast cancer cell lines\u2013lessons for design of combination targeted therapy","volume":"320","author":"Park","year":"2012","journal-title":"Cancer Lett"},{"issue":"1","key":"2022012000285089500_ref38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12885-016-2254-9","article-title":"Nuclear expression of Lyn, a Src family kinase member, is associated with poor prognosis in renal cancer patients","volume":"16","author":"Roseweir","year":"2016","journal-title":"BMC Cancer"},{"issue":"136","key":"2022012000285089500_ref39","doi-asserted-by":"publisher","DOI":"10.1126\/scitranslmed.3003513","article-title":"Kinase impaired BRAF mutations confer lung cancer sensitivity to Dasatinib","volume":"4","author":"Sen","year":"2012","journal-title":"Sci Transl Med"},{"issue":"8","key":"2022012000285089500_ref40","first-page":"1723","article-title":"AKT inhibitor, GSK690693, induces growth inhibition and apoptosis in acute lymphoblastic leukemia cell lines","volume":"113","author":"Levy","year":"2009","journal-title":"Blood, The Journal of the American Society of Hematology"},{"key":"2022012000285089500_ref41","doi-asserted-by":"crossref","first-page":"103671","DOI":"10.1016\/j.bioorg.2020.103671","article-title":"Extensive investigation of benzylic N-containing substituents on the pyrrolopyrimidine skeleton as Akt inhibitors with potent anticancer activity","volume":"97","author":"Liu","year":"2020","journal-title":"Bioorg Chem"},{"issue":"7","key":"2022012000285089500_ref42","doi-asserted-by":"crossref","first-page":"e0133219","DOI":"10.1371\/journal.pone.0133219","article-title":"Decoupling of the PI3K pathway via mutation necessitates combinatorial treatment in HER2+ breast cancer","volume":"10","author":"Korkola","year":"2015","journal-title":"PLoS One"},{"key":"2022012000285089500_ref43","doi-asserted-by":"publisher","first-page":"929","DOI":"10.1109\/ICDM.2018.00113","volume-title":"International Conference on Data Mining (ICDM)","author":"Derr","year":"2018"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/1\/bbab457\/42229967\/bbab457.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/1\/bbab457\/42229967\/bbab457.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,20]],"date-time":"2022-01-20T00:30:06Z","timestamp":1642638606000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbab457\/6415314"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,1]]},"references-count":43,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1,17]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbab457","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,1]]},"published":{"date-parts":[[2021,11,1]]},"article-number":"bbab457"}}