{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T05:04:21Z","timestamp":1732511061916,"version":"3.28.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,24]],"date-time":"2024-11-24T00:00:00Z","timestamp":1732406400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,11,24]],"date-time":"2024-11-24T00:00:00Z","timestamp":1732406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"DOI":"10.1007\/s44163-024-00202-8","type":"journal-article","created":{"date-parts":[[2024,11,24]],"date-time":"2024-11-24T14:37:34Z","timestamp":1732459054000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Identifying selective PDHK inhibitors using coupled tensor matrix completion and experimental validation"],"prefix":"10.1007","volume":"4","author":[{"given":"Flora","family":"Rajaei","sequence":"first","affiliation":[]},{"given":"Peter","family":"Toogood","sequence":"additional","affiliation":[]},{"given":"Renju","family":"Jacob","sequence":"additional","affiliation":[]},{"given":"Mason","family":"Baber","sequence":"additional","affiliation":[]},{"given":"Mya","family":"Gough","sequence":"additional","affiliation":[]},{"given":"Harm","family":"Derksen","sequence":"additional","affiliation":[]},{"given":"Emily","family":"Wittrup","sequence":"additional","affiliation":[]},{"given":"Kayvan","family":"Najarian","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,24]]},"reference":[{"key":"202_CR1","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1038\/s41571-023-00840-4","volume":"21","author":"L Morrison","year":"2024","unstructured":"Morrison L, Loibl S, Turner NC. The cdk4\/6 inhibitor revolution\u2014a game-changing era for breast cancer treatment. Nat Rev Clin Oncol. 2024;21:89\u2013105.","journal-title":"Nat Rev Clin Oncol"},{"key":"202_CR2","doi-asserted-by":"publisher","first-page":"1537","DOI":"10.1158\/1078-0432.CCR-14-2034","volume":"21","author":"Q Yang","year":"2015","unstructured":"Yang Q, Modi P, Newcomb T, Qu\u00e9va C, Gandhi V. Idelalisib: first-in-class pi3k delta inhibitor for the treatment of chronic lymphocytic leukemia, small lymphocytic leukemia, and follicular lymphoma. Clin Cancer Res. 2015;21:1537\u201342.","journal-title":"Clin Cancer Res"},{"key":"202_CR3","doi-asserted-by":"publisher","first-page":"1671","DOI":"10.1007\/s40265-022-01796-y","volume":"82","author":"SM Hoy","year":"2022","unstructured":"Hoy SM. Deucravacitinib: first approval. Drugs. 2022;82:1671\u20139.","journal-title":"Drugs"},{"key":"202_CR4","doi-asserted-by":"publisher","first-page":"188","DOI":"10.4093\/dmj.2015.39.3.188","volume":"39","author":"NH Jeoung","year":"2015","unstructured":"Jeoung NH. Pyruvate dehydrogenase kinases: therapeutic targets for diabetes and cancers. Diabetes Metab J. 2015;39:188.","journal-title":"Diabetes Metab J"},{"key":"202_CR5","first-page":"188568188568","volume":"1876","author":"S Anwar","year":"2021","unstructured":"Anwar S, Shamsi A, Mohammad T, Islam A, Hassan MI. Targeting pyruvate dehydrogenase kinase signaling in the development of effective cancer therapy. Biochim Biophys Acta BBA Rev Cancer. 2021;1876:188568188568.","journal-title":"Biochim Biophys Acta BBA Rev Cancer"},{"key":"202_CR6","doi-asserted-by":"publisher","first-page":"BSR20204402","DOI":"10.1042\/BSR20204402","volume":"41","author":"X Wang","year":"2021","unstructured":"Wang X, Shen X, Yan Y, Li H. Pyruvate dehydrogenase kinases (pdks): an overview toward clinical applications. Biosci Rep. 2021;41:BSR20204402.","journal-title":"Biosci Rep"},{"key":"202_CR7","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1074\/jbc.M116.754127","volume":"292","author":"C Crewe","year":"2017","unstructured":"Crewe C, Schafer C, Lee I, Kinter M, Szweda LI. Regulation of pyruvate dehydrogenase kinase 4 in the heart through degradation by the lon protease in response to mitochondrial substrate availability. J Biol Chem. 2017;292:305\u201312.","journal-title":"J Biol Chem"},{"issue":"1","key":"202_CR8","first-page":"8201079","volume":"2019","author":"T Tataranni","year":"2019","unstructured":"Tataranni T, Piccoli C, et al. Dichloroacetate (dca) and cancer: an overview towards clinical applications. Oxidative Med Cell Long. 2019;2019(1):8201079.","journal-title":"Oxidative Med Cell Long"},{"key":"202_CR9","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0071997","volume":"8","author":"R Tao","year":"2013","unstructured":"Tao R, Xiong X, Harris RA, White MF, Dong XC. Genetic inactivation of pyruvate dehydrogenase kinases improves hepatic insulin resistance induced diabetes. PloS ONE. 2013;8: e71997.","journal-title":"PloS ONE"},{"key":"202_CR10","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1093\/bib\/bbz157","volume":"22","author":"M Bagherian","year":"2021","unstructured":"Bagherian M, et al. Machine learning approaches and databases for prediction of drug\u2013target interaction: a survey paper. Brief Bioinform. 2021;22:247\u201369.","journal-title":"Brief Bioinform"},{"key":"202_CR11","doi-asserted-by":"publisher","first-page":"2033","DOI":"10.2174\/0929867327666200526142958","volume":"28","author":"W Shan","year":"2021","unstructured":"Shan W, Li X, Yao H, Lin K. Convolutional neural network-based virtual screening. Curr Med Chem. 2021;28:2033\u201347.","journal-title":"Curr Med Chem"},{"key":"202_CR12","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.ymeth.2022.08.016","volume":"206","author":"W Wang","year":"2022","unstructured":"Wang W, et al. Gchn-dti: predicting drug-target interactions by graph convolution on heterogeneous networks. Methods. 2022;206:101\u20137.","journal-title":"Methods"},{"key":"202_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-020-00447-2","volume":"12","author":"MA Thafar","year":"2020","unstructured":"Thafar MA, et al. Dtigems+: drug\u2013target interaction prediction using graph embedding, graph mining, and similarity-based techniques. J Cheminform. 2020;12:1\u201317.","journal-title":"J Cheminform"},{"key":"202_CR14","doi-asserted-by":"publisher","first-page":"2805","DOI":"10.1093\/bioinformatics\/btaa010","volume":"36","author":"X Zeng","year":"2020","unstructured":"Zeng X, et al. Network-based prediction of drug\u2013target interactions using an arbitrary-order proximity embedded deep forest. Bioinformatics. 2020;36:2805\u201312.","journal-title":"Bioinformatics"},{"key":"202_CR15","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. 2018;34:i821\u20139.","journal-title":"Bioinformatics"},{"key":"202_CR16","doi-asserted-by":"publisher","first-page":"2161","DOI":"10.1093\/bib\/bbaa025","volume":"22","author":"M Bagherian","year":"2021","unstructured":"Bagherian M, et al. Coupled matrix\u2013matrix and coupled tensor\u2013matrix completion methods for predicting drug-target interactions. Brief Bioinform. 2021;22:2161\u201371.","journal-title":"Brief Bioinform"},{"key":"202_CR17","doi-asserted-by":"publisher","first-page":"1337","DOI":"10.1093\/bib\/bby002","volume":"20","author":"A Ezzat","year":"2019","unstructured":"Ezzat A, Wu M, Li X-L, Kwoh C-K. Computational prediction of drug\u2013target interactions using chemogenomic approaches: an empirical survey. Brief Bioinform. 2019;20:1337\u201357.","journal-title":"Brief Bioinform"},{"key":"202_CR18","first-page":"2022","volume-title":"Data fusion by matrix completion for exposome target interaction prediction","author":"K Wang","year":"2022","unstructured":"Wang K, et al. Data fusion by matrix completion for exposome target interaction prediction. New York: Cold Spring Harbor Laboratory; 2022. p. 2022\u2013208."},{"key":"202_CR19","unstructured":"Kim R. Coupled matrix-matrix completion in regression of oncologic drug sensitivity. IEEE ACM Trans Comput Biol. 2024."},{"key":"202_CR20","doi-asserted-by":"publisher","DOI":"10.1042\/bst0311168","author":"J Morrell","year":"2003","unstructured":"Morrell J, et al. Azd7545 is a selective inhibitor of pyruvate dehydrogenase kinase 2. Biochem Soc Trans. 2003. https:\/\/doi.org\/10.1042\/bst0311168.","journal-title":"Biochem Soc Trans"},{"key":"202_CR21","doi-asserted-by":"publisher","first-page":"9832","DOI":"10.1021\/jm5010144","volume":"57","author":"T Meng","year":"2014","unstructured":"Meng T, et al. Discovery and optimization of 4, 5-diarylisoxazoles as potent dual inhibitors of pyruvate dehydrogenase kinase and heat shock protein 90. J Med Chem. 2014;57:9832\u201343.","journal-title":"J Med Chem"},{"key":"202_CR22","doi-asserted-by":"publisher","first-page":"12862","DOI":"10.18632\/oncotarget.2656","volume":"5","author":"JD Moore","year":"2014","unstructured":"Moore JD, et al. Ver-246608, a novel pan-isoform ATP competitive inhibitor of pyruvate dehydrogenase kinase, disrupts Warburg metabolism and induces context-dependent cytostasis in cancer cells. Oncotarget. 2014;5:12862.","journal-title":"Oncotarget"},{"key":"202_CR23","doi-asserted-by":"publisher","first-page":"2271","DOI":"10.1021\/acs.jmedchem.6b01478","volume":"60","author":"PA Brough","year":"2017","unstructured":"Brough PA, et al. Application of off-rate screening in the identification of novel pan-isoform inhibitors of pyruvate dehydrogenase kinase. J Med Chem. 2017;60:2271\u201386.","journal-title":"J Med Chem"},{"key":"202_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.bmc.2021.116514","volume":"52","author":"Y Bessho","year":"2021","unstructured":"Bessho Y, et al. Structure-based drug design of novel and highly potent pyruvate dehydrogenase kinase inhibitors. Bioorganic Med Chem. 2021;52: 116514.","journal-title":"Bioorganic Med Chem"},{"key":"202_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.bmc.2021.116283","volume":"44","author":"T Akaki","year":"2021","unstructured":"Akaki T, et al. Fragment-based lead discovery to identify novel inhibitors that target the ATP binding site of pyruvate dehydrogenase kinases. Bioorganic Med Chem. 2021;44: 116283.","journal-title":"Bioorganic Med Chem"},{"key":"202_CR26","doi-asserted-by":"publisher","first-page":"8461","DOI":"10.1021\/acs.jmedchem.9b00565","volume":"62","author":"H Cho","year":"2019","unstructured":"Cho H, et al. Identification of novel resorcinol amide derivatives as potent and specific pyruvate dehydrogenase kinase (PDHK) inhibitors. J Med Chem. 2019;62:8461\u201379.","journal-title":"J Med Chem"},{"key":"202_CR27","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.1038\/nbt.2017","volume":"29","author":"T Anastassiadis","year":"2011","unstructured":"Anastassiadis T, Deacon SW, Devarajan K, Ma H, Peterson JR. Comprehensive assay of kinase catalytic activity reveals features of kinase inhibitor selectivity. Nat Biotechnol. 2011;29:1039\u201345.","journal-title":"Nat Biotechnol"},{"key":"202_CR28","doi-asserted-by":"publisher","first-page":"526","DOI":"10.2331\/suisan.22.526","volume":"22","author":"A Ochiai","year":"1957","unstructured":"Ochiai A. Zoogeographic studies on the soleoid fishes found in Japan and its neighbouring regions. Bull Jpn Soc Sci Fish. 1957;22:526\u201330.","journal-title":"Bull Jpn Soc Sci Fish"},{"key":"202_CR29","doi-asserted-by":"publisher","first-page":"297","DOI":"10.2307\/1932409","volume":"26","author":"LR Dice","year":"1945","unstructured":"Dice LR. Measures of the amount of ecologic association between species. Ecology. 1945;26:297\u2013302.","journal-title":"Ecology"},{"key":"202_CR30","first-page":"1","volume":"5","author":"T Sorensen","year":"1948","unstructured":"Sorensen T. A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on danish commons. Biol Skr. 1948;5:1\u201334.","journal-title":"Biol Skr"},{"key":"202_CR31","volume-title":"The determination and analysis of plankton communities","author":"BH McConnaughey","year":"1964","unstructured":"McConnaughey BH. The determination and analysis of plankton communities. New York: Lembaga Penelitian Laut; 1964."},{"key":"202_CR32","first-page":"190","volume-title":"The principles and practice of numerical taxonomy","author":"RR Sokal","year":"1963","unstructured":"Sokal RR. The principles and practice of numerical taxonomy. New York: JSTOR; 1963. p. 190\u20139."},{"key":"202_CR33","unstructured":"Tanimoto T. An elementary mathematical theory of classification and prediction (International Business Machines Corporation). 1958. https:\/\/books.google.com\/books?id=yp34HAAACAAJ."},{"key":"202_CR34","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1037\/0033-295X.84.4.327","volume":"84","author":"A Tversky","year":"1977","unstructured":"Tversky A. Features of similarity. Psychol Rev. 1977;84:327.","journal-title":"Psychol Rev"},{"key":"202_CR35","unstructured":"Braun-Blanquet J, Fuller G, Conard H. Plant sociology: the study of plant communities. Hafner Publishing Company. 1965. https:\/\/books.google.com\/books?id=qrwgAAAAMAAJ."},{"key":"202_CR36","unstructured":"Hayek LA.\u00a0C. Analysis of amphibian biodiversity data. Measuring and monitoring biological diversity: standard methods for amphibians. Smithsonian institution press: Washington, D.C. 1994."},{"key":"202_CR37","doi-asserted-by":"publisher","first-page":"4156","DOI":"10.1021\/acs.jcim.0c00993","volume":"61","author":"H Safizadeh","year":"2021","unstructured":"Safizadeh H, et al. Improving measures of chemical structural similarity using machine learning on chemical\u2013genetic interactions. J Chem Inform Model. 2021;61:4156\u201372.","journal-title":"J Chem Inform Model"}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-024-00202-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-024-00202-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-024-00202-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,24]],"date-time":"2024-11-24T15:03:35Z","timestamp":1732460615000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-024-00202-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,24]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["202"],"URL":"https:\/\/doi.org\/10.1007\/s44163-024-00202-8","relation":{},"ISSN":["2731-0809"],"issn-type":[{"type":"electronic","value":"2731-0809"}],"subject":[],"published":{"date-parts":[[2024,11,24]]},"assertion":[{"value":"29 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 November 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":"None to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"89"}}