{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T18:13:38Z","timestamp":1788632018731,"version":"build-2803163510"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T00:00:00Z","timestamp":1788566400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T00:00:00Z","timestamp":1788566400000},"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":["J Comput Aided Mol Des"],"DOI":"10.1007\/s10822-026-00935-x","type":"journal-article","created":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T05:41:37Z","timestamp":1788586897000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A novel framework for the discovery of MAPK-activated protein kinase 2 (MAPKAPK2) inhibitors using a multi-feature deep learning ensemble"],"prefix":"10.1007","volume":"40","author":[{"given":"Hayden","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Wen","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tony Eight","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun-Hong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Cheng","family":"Chan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chun-Lin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shih-Chung","family":"Yen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei-Chun","family":"HuangFu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiow-Lin","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai-Cheng","family":"Hsu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,5]]},"reference":[{"key":"935_CR1","doi-asserted-by":"publisher","DOI":"10.4061\/2011\/794089","volume":"2011","author":"HC Cheng","year":"2011","unstructured":"Cheng HC, Qi RZ, Paudel H, Zhu HJ (2011) Regulation and function of protein kinases and phosphatases. Enzyme Res 2011:794089","journal-title":"Enzyme Res"},{"key":"935_CR2","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1186\/s13046-019-1115-1","volume":"38","author":"S Soni","year":"2019","unstructured":"Soni S, Anand P, Padwad YS (2019) MAPKAPK2: the master regulator of RNA-binding proteins modulates transcript stability and tumor progression. J Exp Clin Cancer Res 38:121","journal-title":"J Exp Clin Cancer Res"},{"key":"935_CR3","doi-asserted-by":"publisher","first-page":"3231","DOI":"10.1091\/mbc.e11-05-0400","volume":"22","author":"W Cai","year":"2011","unstructured":"Cai W, Rudolph JL, Harrison SM, Jin L, Frantz AL, Harrison DA, Andres DA (2011) An evolutionarily conserved Rit GTPase-p38 MAPK signaling pathway mediates oxidative stress resistance. Mol Biol Cell 22:3231\u20133241","journal-title":"Mol Biol Cell"},{"key":"935_CR4","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1016\/j.semcancer.2022.03.025","volume":"86","author":"W Li","year":"2022","unstructured":"Li W, Deng X, Chen J (2022) RNA-binding proteins in regulating mRNA stability and translation: roles and mechanisms in cancer. Semin Cancer Biol 86:664\u2013677","journal-title":"Semin Cancer Biol"},{"key":"935_CR5","doi-asserted-by":"publisher","first-page":"101","DOI":"10.3390\/genes4020101","volume":"4","author":"U Moens","year":"2013","unstructured":"Moens U, Kostenko S, Sveinbjornsson B (2013) The role of mitogen-activated protein kinase-activated protein kinases (MAPKAPKs) in inflammation. Genes (Basel) 4:101\u2013133","journal-title":"Genes (Basel)"},{"key":"935_CR6","doi-asserted-by":"publisher","first-page":"56","DOI":"10.3390\/cancers12071791","volume":"12","author":"C Rebe","year":"2020","unstructured":"Rebe C, Ghiringhelli F (2020) Interleukin-1beta and Cancer. Cancers (Basel) 12:56","journal-title":"Cancers (Basel)"},{"key":"935_CR7","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13030564","author":"MF Mercogliano","year":"2021","unstructured":"Mercogliano MF, Bruni S, Mauro F, Elizalde PV, Schillaci R (2021) Harnessing tumor necrosis factor alpha to achieve effective cancer immunotherapy. Cancers (Basel). https:\/\/doi.org\/10.3390\/cancers13030564","journal-title":"Cancers (Basel)"},{"key":"935_CR8","doi-asserted-by":"publisher","first-page":"7329","DOI":"10.1038\/s41388-019-0945-9","volume":"38","author":"KL Berggren","year":"2019","unstructured":"Berggren KL, Restrepo Cruz S, Hixon MD, Cowan AT, Keysar SB, Craig S, James J, Barry M, Ozbun MA, Jimeno A, McCance DJ, Beswick EJ, Gan GN (2019) MAPKAPK2 (MK2) inhibition mediates radiation-induced inflammatory cytokine production and tumor growth in head and neck squamous cell carcinoma. Oncogene 38:7329\u20137341","journal-title":"Oncogene"},{"key":"935_CR9","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1186\/s13046-019-1167-2","volume":"38","author":"S Soni","year":"2019","unstructured":"Soni S, Saroch MK, Chander B, Tirpude NV, Padwad YS (2019) MAPKAPK2 plays a crucial role in the progression of head and neck squamous cell carcinoma by regulating transcript stability. J Exp Clin Cancer Res 38:175","journal-title":"J Exp Clin Cancer Res"},{"key":"935_CR10","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0246600","volume":"16","author":"L Ozcan","year":"2021","unstructured":"Ozcan L, Kasikara C, Yurdagul A Jr., Kuriakose G, Hubbard B, Serrano-Wu MH, Tabas I (2021) Allosteric MAPKAPK2 inhibitors improve plaque stability in advanced atherosclerosis. PLoS ONE 16:e0246600","journal-title":"PLoS ONE"},{"key":"935_CR11","doi-asserted-by":"publisher","first-page":"3396","DOI":"10.2337\/db14-1945","volume":"64","author":"L Ozcan","year":"2015","unstructured":"Ozcan L, Xu X, Deng SX, Ghorpade DS, Thomas T, Cremers S, Hubbard B, Serrano-Wu MH, Gaestel M, Landry DW, Tabas I (2015) Treatment of obese insulin-resistant mice with an allosteric MAPKAPK2\/3 inhibitor lowers blood glucose and improves insulin sensitivity. Diabetes 64:3396\u20133405","journal-title":"Diabetes"},{"key":"935_CR12","doi-asserted-by":"publisher","first-page":"3609","DOI":"10.1021\/acs.jmedchem.5b01457","volume":"59","author":"M Fiore","year":"2016","unstructured":"Fiore M, Forli S, Manetti F (2016) Targeting mitogen-activated protein kinase-activated protein kinase 2 (MAPKAPK2, MK2): medicinal chemistry efforts to lead small molecule inhibitors to clinical trials. J Med Chem 59:3609\u20133634","journal-title":"J Med Chem"},{"key":"935_CR13","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1177\/2472555220949495","volume":"26","author":"GHS Dreiman","year":"2021","unstructured":"Dreiman GHS, Bictash M, Fish PV, Griffin L, Svensson F (2021) Changing the HTS paradigm: AI-driven iterative screening for hit finding. SLAS Discov 26:257\u2013262","journal-title":"SLAS Discov"},{"key":"935_CR14","doi-asserted-by":"publisher","first-page":"1315","DOI":"10.1007\/s11030-021-10217-3","volume":"25","author":"R Gupta","year":"2021","unstructured":"Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P (2021) Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers 25:1315\u20131360","journal-title":"Mol Divers"},{"key":"935_CR15","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.2174\/1381612829666230412084137","volume":"29","author":"S Sumathi","year":"2023","unstructured":"Sumathi S, Suganya K, Swathi K, Sudha B, Poornima A, Varghese CA, Aswathy R (2023) A review on deep learning-driven drug discovery: strategies, tools and applications. Curr Pharm Des 29:1013\u20131025","journal-title":"Curr Pharm Des"},{"key":"935_CR16","doi-asserted-by":"publisher","DOI":"10.3389\/fmicb.2021.739684","volume":"12","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Ye T, Xi H, Juhas M, Li J (2021) Deep learning driven drug discovery: tackling severe acute respiratory syndrome coronavirus 2. Front Microbiol 12:739684","journal-title":"Front Microbiol"},{"key":"935_CR17","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1021\/ci00057a005","volume":"28","author":"D Weininger","year":"1988","unstructured":"Weininger D (1988) SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Comput Sci 28:31\u201336","journal-title":"J Chem Inf Comput Sci"},{"key":"935_CR18","doi-asserted-by":"publisher","unstructured":"RDKit: Open-source cheminformatics. https:\/\/doi.org\/10.5281\/zenodo.591637","DOI":"10.5281\/zenodo.591637"},{"key":"935_CR19","unstructured":"E. Noutahi, C. Wognum, H. Mary, H. Hounwanou, K.M. Kovary, D. Gilmour, r. thibaultvarin, J. Burns, J. St-Laurent, T, DomInvivo, S. Maheshkar, m. rbyrne, datamol-io\/molfeat: 0.9.4, Zenodo, 2023."},{"key":"935_CR20","unstructured":"K. Leino, E. Black, M. Fredrikson, S. Sen, A. Datta, Feature-Wise Bias Amplification, 2018, pp. arXiv:1812.08999."},{"issue":"2020","key":"935_CR21","doi-asserted-by":"publisher","first-page":"3525","DOI":"10.1039\/D0CS00098A","volume":"49","author":"N Muratov","year":"2020","unstructured":"Muratov N, Bajorath J, Sheridan RP, Tetko IV, Filimonov D, Poroikov V, Oprea TI, Baskin A II, Varnek A, Roitberg O, Isayev S, Curtarolo D, Fourches Y, Cohen A, Aspuru-Guzik DA, Winkler D, Agrafiotis A, Cherkasov AT (2020) QSAR without borders. Chem Soc Rev 49(2020):3525\u20133564","journal-title":"Chem Soc Rev"},{"key":"935_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122778","volume":"244","author":"AA Khan","year":"2024","unstructured":"Khan AA, Chaudhari O, Chandra R (2024) A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation. Expert Syst Appl 244:122778","journal-title":"Expert Syst Appl"},{"key":"935_CR23","unstructured":"Z. Ghorbanali, F. Zare-Mirakabad, B. Mohammadpour, DRP-VEM: Drug repositioning prediction using voting ensemble, 2021, pp. arXiv:2110.01403."},{"key":"935_CR24","doi-asserted-by":"publisher","DOI":"10.1002\/pro.5007","volume":"33","author":"TE Lin","year":"2024","unstructured":"Lin TE, Yen D, HuangFu WC, Wu YW, Hsu JY, Yen SC, Sung TY, Hsieh JH, Pan SL, Yang CR, Huang WJ, Hsu KC (2024) An ensemble machine learning model generates a focused screening library for the identification of CDK8 inhibitors. Protein Sci 33:e5007","journal-title":"Protein Sci"},{"key":"935_CR25","doi-asserted-by":"publisher","first-page":"W612","DOI":"10.1093\/nar\/gkv352","volume":"43","author":"M Davies","year":"2015","unstructured":"Davies M, Nowotka M, Papadatos G, Dedman N, Gaulton A, Atkinson F, Bellis L, Overington JP (2015) ChEMBL web services: streamlining access to drug discovery data and utilities. Nucleic Acids Res 43:W612-620","journal-title":"Nucleic Acids Res"},{"key":"935_CR26","doi-asserted-by":"publisher","first-page":"D1388","DOI":"10.1093\/nar\/gkaa971","volume":"49","author":"S Kim","year":"2021","unstructured":"Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, Li Q, Shoemaker BA, Thiessen PA, Yu B, Zaslavsky L, Zhang J, Bolton EE (2021) PubChem in 2021: new data content and improved web interfaces. Nucleic Acids Res 49:D1388\u2013D1395","journal-title":"Nucleic Acids Res"},{"key":"935_CR27","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1021\/acs.jmedchem.6b01611","volume":"60","author":"B Merget","year":"2017","unstructured":"Merget B, Turk S, Eid S, Rippmann F, Fulle S (2017) Profiling prediction of kinase inhibitors: toward the virtual assay. J Med Chem 60:474\u2013485","journal-title":"J Med Chem"},{"key":"935_CR28","doi-asserted-by":"publisher","first-page":"340","DOI":"10.2174\/1568026613666131127160819","volume":"14","author":"DH Drewry","year":"2014","unstructured":"Drewry DH, Willson TM, Zuercher WJ (2014) Seeding collaborations to advance kinase science with the GSK published kinase inhibitor set (PKIS). Curr Top Med Chem 14:340\u2013342","journal-title":"Curr Top Med Chem"},{"issue":"2016","key":"935_CR29","doi-asserted-by":"publisher","first-page":"1654","DOI":"10.1021\/acs.jcim.6b00122","volume":"56","author":"S Christmann-Franck","year":"2016","unstructured":"Christmann-Franck S, van Westen GJ, Papadatos G, Beltran Escudie F, Roberts A, Overington JP, Domine D (2016) Unprecedently large-scale kinase inhibitor set enabling the accurate prediction of compound-kinase activities: a way toward selective promiscuity by design. J Chem Inf Model 56(2016):1654\u20131675","journal-title":"J Chem Inf Model"},{"key":"935_CR30","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1021\/ci400709d","volume":"54","author":"J Tang","year":"2014","unstructured":"Tang J, Szwajda A, Shakyawar S, Xu T, Hintsanen P, Wennerberg K, Aittokallio T (2014) Making sense of large-scale kinase inhibitor bioactivity data sets: a comparative and integrative analysis. J Chem Inf Model 54:735\u2013743","journal-title":"J Chem Inf Model"},{"key":"935_CR31","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1038\/s41573-023-00832-0","volume":"23","author":"A Tropsha","year":"2024","unstructured":"Tropsha A, Isayev O, Varnek A, Schneider G, Cherkasov A (2024) Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR. Nat Rev Drug Discov 23:141\u2013155","journal-title":"Nat Rev Drug Discov"},{"key":"935_CR32","unstructured":"L. Buitinck, G. Louppe, M. Blondel, F. Pedregosa, A. Mueller, O. Grisel, V. Niculae, P. Prettenhofer, A. Gramfort, J. Grobler, R. Layton, J. Vanderplas, A. Joly, B. Holt, G. Varoquaux, API design for machine learning software: experiences from the scikit-learn project, 2013, pp. arXiv:1309.0238."},{"key":"935_CR33","unstructured":"F. Chollet, Keras, (2015). https:\/\/keras.io"},{"key":"935_CR34","unstructured":"A.H. Crabb\u00e9, T. Cahy, B. Somers, L.P. Verbeke, F. Van Coillie, Neural Network MLPClassifier (Version 3.5.0) [Keras], 2020."},{"key":"935_CR35","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/S0169-409X(00)00129-0","volume":"46","author":"CA Lipinski","year":"2001","unstructured":"Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 46:3\u201326","journal-title":"Adv Drug Deliv Rev"},{"key":"935_CR36","doi-asserted-by":"publisher","first-page":"2719","DOI":"10.1021\/jm901137j","volume":"53","author":"JB Baell","year":"2010","unstructured":"Baell JB, Holloway GA (2010) New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J Med Chem 53:2719\u20132740","journal-title":"J Med Chem"},{"key":"935_CR37","unstructured":"L.L.C. Schr\u00f6dinger, Schr\u00f6dinger Release 2022\u20133: Maestro, Schr\u00f6dinger, LLC, New York, NY, 2021."},{"key":"935_CR38","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1093\/nar\/28.1.235","volume":"28","author":"HM Berman","year":"2000","unstructured":"Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The Protein Data Bank. Nucleic Acids Res 28:235\u2013242","journal-title":"Nucleic Acids Res"},{"key":"935_CR39","unstructured":"BIOVIA Pipeline Pilot, 2024. http:\/\/accelrys.com\/products\/collaborative-science\/biovia-pipeline-pilot"},{"key":"935_CR40","unstructured":"BIOVIA, Dassault Syst\u00e8mes, Discovery Studio Modeling Environment, , San Diego: Dassault Syst\u00e8mes, 2021"},{"key":"935_CR41","doi-asserted-by":"publisher","DOI":"10.1038\/srep42717","volume":"7","author":"A Daina","year":"2017","unstructured":"Daina A, Michielin O, Zoete V (2017) SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 7:42717","journal-title":"Sci Rep"},{"key":"935_CR42","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1021\/acs.jcim.3c01774","volume":"64","author":"P Tossou","year":"2024","unstructured":"Tossou P, Wognum C, Craig M, Mary H, Noutahi E (2024) Real-world molecular out-of-distribution: specification and investigation. J Chem Inf Model 64:697\u2013711","journal-title":"J Chem Inf Model"},{"key":"935_CR43","doi-asserted-by":"publisher","first-page":"6560","DOI":"10.1021\/jm301916b","volume":"56","author":"T Zhu","year":"2013","unstructured":"Zhu T, Cao S, Su PC, Patel R, Shah D, Chokshi HB, Szukala R, Johnson ME, Hevener KE (2013) Hit identification and optimization in virtual screening: practical recommendations based on a critical literature analysis. J Med Chem 56:6560\u20136572","journal-title":"J Med Chem"},{"key":"935_CR44","volume-title":"KNIME: the Konstanz information miner","author":"MR Berthold","year":"2007","unstructured":"Berthold MR, Cebron N, Dill F, Gabriel TR, K\u00f6tter T, Meinl T, Ohl P, Sieb C, Thiel K, Wiswedel B (2007) KNIME: the Konstanz information miner. Springer"},{"key":"935_CR45","unstructured":"Schr\u00f6dinger, LLC. , The PyMOL Molecular Graphics System, Version 1.8, 2015."}],"container-title":["Journal of Computer-Aided Molecular Design"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10822-026-00935-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10822-026-00935-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10822-026-00935-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T18:02:26Z","timestamp":1788631346000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10822-026-00935-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,5]]},"references-count":45,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["935"],"URL":"https:\/\/doi.org\/10.1007\/s10822-026-00935-x","relation":{},"ISSN":["1573-4951"],"issn-type":[{"value":"1573-4951","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,5]]},"assertion":[{"value":"3 March 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 August 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 September 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"All data used in this study were obtained from publicly available databases. Ethical approval and informed consent were not required.","order":1,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"230"}}