{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T22:29:17Z","timestamp":1773008957412,"version":"3.50.1"},"reference-count":56,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T00:00:00Z","timestamp":1736726400000},"content-version":"vor","delay-in-days":52,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2023-00257479"],"award-info":[{"award-number":["RS-2023-00257479"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003661","name":"Korea Institute for Advancement of Technology","doi-asserted-by":"publisher","award":["P0024560"],"award-info":[{"award-number":["P0024560"]}],"id":[{"id":"10.13039\/501100003661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Combination therapies have emerged as a promising approach for treating complex diseases, particularly cancer. However, predicting the efficacy and safety profiles of these therapies remains a significant challenge, primarily because of the complex interactions among drugs and their wide-ranging effects. To address this issue, we introduce DD-PRiSM (Decomposition of Drug-Pair Response into Synergy and Monotherapy effect), a deep-learning pipeline that predicts the effects of combination therapy. DD-PRiSM consists of two predictive models. The first is the Monotherapy model, which predicts parameters of the drug response curve based on drug structure and cell line gene expression. This reconstructed curve is then used to predict cell viability at the given drug dosage. The second is the Combination therapy model, which predicts the efficacy of drug combinations by analyzing individual drug effects and their synergistic interactions with a specific dosage level of individual drugs. The efficacy of DD-PRiSM is demonstrated through its performance metrics, achieving a root mean square error of 0.0854, a Pearson correlation coefficient of 0.9063, and an R2 of 0.8209 for unseen pairs. Furthermore, DD-PRiSM distinguishes itself by its capability to decompose combination therapy efficacy, successfully identifying synergistic drug pairs. We demonstrated synergistic responses vary across cancer types and identified hub drugs that trigger synergistic effects. Finally, we suggested a promising drug pair through our case study.<\/jats:p>","DOI":"10.1093\/bib\/bbae717","type":"journal-article","created":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T03:33:37Z","timestamp":1736739217000},"source":"Crossref","is-referenced-by-count":4,"title":["DD-PRiSM: a deep learning framework for decomposition and prediction of synergistic drug combinations"],"prefix":"10.1093","volume":"26","author":[{"given":"Iljung","family":"Jin","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju 61005 ,","place":["Republic of Korea"]}]},{"given":"Songyeon","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju 61005 ,","place":["Republic of Korea"]}]},{"given":"Martin","family":"Schmuhalek","sequence":"additional","affiliation":[{"name":"AI Graduate School, Gwangju Institute of Science and Technology (GIST) , Buk-gu, Gwangju 61005 ,","place":["Republic of Korea"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5109-9114","authenticated-orcid":false,"given":"Hojung","family":"Nam","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju 61005 ,","place":["Republic of Korea"]},{"name":"AI Graduate School, Gwangju Institute of Science and Technology (GIST) , Buk-gu, Gwangju 61005 ,","place":["Republic of Korea"]},{"name":"Center for AI-Applied High Efficiency Drug Discovery (AHEDD), Gwangju Institute of Science and Technology (GIST) , Buk-gu, Gwangju 61005 ,","place":["Republic of Korea"]}]}],"member":"286","published-online":{"date-parts":[[2025,1,12]]},"reference":[{"key":"2025011303332121900_ref1","doi-asserted-by":"publisher","first-page":"2367","DOI":"10.1016\/j.drudis.2021.05.008","article-title":"Drug combination therapy for emerging viral diseases","volume":"26","author":"Shyr","year":"2021","journal-title":"Drug Discov Today"},{"key":"2025011303332121900_ref2","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1038\/s41573-022-00615-z","article-title":"Rational combinations of targeted cancer therapies: Background, advances and challenges","volume":"22","author":"Jin","year":"2023","journal-title":"Nat Rev Drug Discov"},{"key":"2025011303332121900_ref3","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1186\/s13045-021-01164-5","article-title":"Combination strategies to maximize the benefits of cancer immunotherapy","volume":"14","author":"Zhu","year":"2021","journal-title":"J Hematol Oncol"},{"key":"2025011303332121900_ref4","doi-asserted-by":"publisher","first-page":"38022","DOI":"10.18632\/oncotarget.16723","article-title":"Combination therapy in combating cancer","volume":"8","author":"Mokhtari","year":"2017","journal-title":"Oncotarget"},{"key":"2025011303332121900_ref5","doi-asserted-by":"publisher","first-page":"3564","DOI":"10.1158\/0008-5472.CAN-17-0489","article-title":"The National Cancer Institute ALMANAC: A comprehensive screening resource for the detection of anticancer drug pairs with enhanced therapeutic activity","volume":"77","author":"Holbeck","year":"2017","journal-title":"Cancer Res"},{"key":"2025011303332121900_ref6","doi-asserted-by":"publisher","first-page":"1538","DOI":"10.1093\/bioinformatics\/btx806","article-title":"DeepSynergy: Predicting anti-cancer drug synergy with deep learning","volume":"34","author":"Preuer","year":"2018","journal-title":"Bioinformatics"},{"key":"2025011303332121900_ref7","doi-asserted-by":"publisher","first-page":"2334","DOI":"10.1109\/TCBB.2021.3086702","article-title":"MatchMaker: A deep learning framework for drug synergy prediction","volume":"19","author":"Kuru","year":"2021","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2025011303332121900_ref8","doi-asserted-by":"publisher","first-page":"bbab390","DOI":"10.1093\/bib\/bbab390","article-title":"DeepDDS: Deep graph neural network with attention mechanism to predict synergistic drug combinations","volume":"23","author":"Wang","year":"2022","journal-title":"Brief Bioinform"},{"key":"2025011303332121900_ref9","doi-asserted-by":"publisher","first-page":"2854","DOI":"10.1021\/acs.jcim.3c00709","article-title":"AttenSyn: An attention-based deep graph neural network for anticancer synergistic drug combination prediction","volume":"64","author":"Wang","year":"2023","journal-title":"J Chem Inf Model"},{"key":"2025011303332121900_ref10","first-page":"285","article-title":"The problem of synergism and antagonism of combined drugs","volume":"3","author":"Loewe","year":"1953","journal-title":"Arzneimittelforschung"},{"key":"2025011303332121900_ref11","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1111\/j.1744-7348.1939.tb06990.x","article-title":"The toxicity of poisons applied jointly 1","volume":"26","author":"Bliss","year":"1939","journal-title":"Ann Appl Biol"},{"key":"2025011303332121900_ref12","doi-asserted-by":"publisher","first-page":"W43","DOI":"10.1093\/nar\/gkz337","article-title":"DrugComb: An integrative cancer drug combination data portal","volume":"47","author":"Zagidullin","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2025011303332121900_ref13","doi-asserted-by":"publisher","first-page":"5848","DOI":"10.1038\/s41467-020-19563-6","article-title":"Computationally predicting clinical drug combination efficacy with cancer cell line screens and independent drug action","volume":"11","author":"Ling","year":"2020","journal-title":"Nat Commun"},{"key":"2025011303332121900_ref14","doi-asserted-by":"publisher","first-page":"6136","DOI":"10.1038\/s41467-020-19950-z","article-title":"Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects","volume":"11","author":"Julkunen","year":"2020","journal-title":"Nat Commun"},{"key":"2025011303332121900_ref15","doi-asserted-by":"publisher","first-page":"i93","DOI":"10.1093\/bioinformatics\/btab308","article-title":"Modeling drug combination effects via latent tensor reconstruction","volume":"37","author":"Wang","year":"2021","journal-title":"Bioinformatics"},{"key":"2025011303332121900_ref16","doi-asserted-by":"publisher","first-page":"1678","DOI":"10.1016\/j.cell.2017.11.009","article-title":"Combination cancer therapy can confer benefit via patient-to-patient variability without drug additivity or synergy","volume":"171","author":"Palmer","year":"2017","journal-title":"Cell"},{"key":"2025011303332121900_ref17","author":"Szedmak"},{"key":"2025011303332121900_ref18","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1038\/nrc1951","article-title":"The NCI60 human tumour cell line anticancer drug screen","volume":"6","author":"Shoemaker","year":"2006","journal-title":"Nat Rev Cancer"},{"key":"2025011303332121900_ref19","doi-asserted-by":"publisher","first-page":"1155","DOI":"10.1158\/1535-7163.MCT-15-0843","article-title":"An unbiased oncology compound screen to identify novel combination strategies","volume":"15","author":"O'Neil","year":"2016","journal-title":"Mol Cancer Ther"},{"key":"2025011303332121900_ref20","volume-title":"RDKit: Open-Source Cheminformatics","author":"Landrum","year":"2010"},{"key":"2025011303332121900_ref21","doi-asserted-by":"publisher","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"},{"key":"2025011303332121900_ref22","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1016\/j.cell.2017.06.010","article-title":"Defining a cancer dependency map","volume":"170","author":"Tsherniak","year":"2017","journal-title":"Cell"},{"key":"2025011303332121900_ref23","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1093\/nar\/28.1.27","article-title":"KEGG: Kyoto encyclopedia of genes and genomes","volume":"28","author":"Kanehisa","year":"2000","journal-title":"Nucleic Acids Res"},{"key":"2025011303332121900_ref24","doi-asserted-by":"publisher","first-page":"15545","DOI":"10.1073\/pnas.0506580102","article-title":"Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles","volume":"102","author":"Subramanian","year":"2005","journal-title":"Proc Natl Acad Sci"},{"key":"2025011303332121900_ref25","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1016\/j.cels.2015.12.004","article-title":"The molecular signatures database hallmark gene set collection","volume":"1","author":"Liberzon","year":"2015","journal-title":"Cell Syst"},{"key":"2025011303332121900_ref26","doi-asserted-by":"publisher","first-page":"691","DOI":"10.2217\/pgs.16.15","article-title":"Multilevel models improve precision and speed of IC50 estimates","volume":"17","author":"Vis","year":"2016","journal-title":"Pharmacogenomics"},{"key":"2025011303332121900_ref27","doi-asserted-by":"publisher","first-page":"3858","DOI":"10.1021\/acs.jcim.1c00706","article-title":"HiDRA: Hierarchical network for drug response prediction with attention","volume":"61","author":"Jin","year":"2021","journal-title":"J Chem Inf Model"},{"key":"2025011303332121900_ref28","first-page":"012004","article-title":"Evaluation of error-and correlation-based loss functions for multitask learning dimensional speech emotion recognition","volume-title":"J Phys Conf Ser","author":"Atmaja","year":"2021"},{"key":"2025011303332121900_ref29","doi-asserted-by":"publisher","author":"Loshchilov","DOI":"10.48550\/arXiv.1711.05101"},{"key":"2025011303332121900_ref30","doi-asserted-by":"publisher","first-page":"bbac100","DOI":"10.1093\/bib\/bbac100","article-title":"DeepTTA: A transformer-based model for predicting cancer drug response","volume":"23","author":"Jiang","year":"2022","journal-title":"Brief Bioinform"},{"key":"2025011303332121900_ref31","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: Fundamental algorithms for scientific computing in Python","volume":"17","author":"Virtanen","year":"2020","journal-title":"Nat Methods"},{"key":"2025011303332121900_ref32","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac100","article-title":"Pytorch: An imperative style, high-performance deep learning library","volume":"32","author":"Paszke","year":"2019","journal-title":"Adv Neural Inf Process Syst"},{"key":"2025011303332121900_ref33","doi-asserted-by":"publisher","first-page":"1041","DOI":"10.1145\/3324884.3416609","volume-title":"Proceedings of the 35th IEEE\/ACM International Conference on Automated Software Engineering","author":"Berend","year":"2020"},{"key":"2025011303332121900_ref34","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1038\/s41586-022-04437-2","article-title":"Effective drug combinations in breast, colon and pancreatic cancer cells","volume":"603","author":"Jaaks","year":"2022","journal-title":"Nature"},{"key":"2025011303332121900_ref35","doi-asserted-by":"publisher","first-page":"2807","DOI":"10.1016\/j.csbj.2022.05.055","article-title":"Systematic review of computational methods for drug combination prediction","volume":"20","author":"Kong","year":"2022","journal-title":"Comput Struct Biotechnol J"},{"key":"2025011303332121900_ref36","doi-asserted-by":"publisher","DOI":"10.1145\/502512.502550","volume-title":"Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining","author":"Dhillon","year":"2001"},{"key":"2025011303332121900_ref37","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"2025011303332121900_ref38","doi-asserted-by":"publisher","first-page":"2407","DOI":"10.1158\/0008-5472.CAN-17-3644","article-title":"Patient-customized drug combination prediction and testing for T-cell prolymphocytic leukemia patients","volume":"78","author":"He","year":"2018","journal-title":"Cancer Res"},{"key":"2025011303332121900_ref39","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1016\/j.trecan.2022.06.009","article-title":"Drug independence and the curability of cancer by combination chemotherapy","volume":"8","author":"Pomeroy","year":"2022","journal-title":"Trends Cancer"},{"key":"2025011303332121900_ref40","doi-asserted-by":"publisher","first-page":"2306","DOI":"10.1200\/JCO.2017.76.7228","article-title":"Dasatinib plus intensive chemotherapy in children, adolescents, and young adults with Philadelphia chromosome\u2013positive acute lymphoblastic leukemia: Results of children\u2019s oncology group trial AALL0622","volume":"36","author":"Slayton","year":"2018","journal-title":"J Clin Oncol"},{"key":"2025011303332121900_ref41","doi-asserted-by":"publisher","first-page":"696","DOI":"10.1182\/bloodadvances.2019000492","article-title":"Combination of dasatinib with chemotherapy in previously untreated core binding factor acute myeloid leukemia: CALGB 10801","volume":"4","author":"Marcucci","year":"2020","journal-title":"Blood Adv"},{"key":"2025011303332121900_ref42","doi-asserted-by":"publisher","first-page":"e12","DOI":"10.1016\/S2352-3026(14)00026-X","article-title":"Sequential azacitidine and lenalidomide in patients with high-risk myelodysplastic syndromes and acute myeloid leukaemia: A single-arm, phase 1\/2 study","volume":"2","author":"DiNardo","year":"2015","journal-title":"Lancet Haematol"},{"key":"2025011303332121900_ref43","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1038\/leu.2016.303","article-title":"A clinical trial for patients with acute myeloid leukemia or myelodysplastic syndromes not eligible for standard clinical trials","volume":"31","author":"Montalban-Bravo","year":"2017","journal-title":"Leukemia"},{"key":"2025011303332121900_ref44","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s00280-005-0135-z","article-title":"Bortezomib interactions with chemotherapy agents in acute leukemia in vitro","volume":"58","author":"Horton","year":"2006","journal-title":"Cancer Chemother Pharmacol"},{"key":"2025011303332121900_ref45","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.lungcan.2007.02.003","article-title":"Pemetrexed combined with paclitaxel in patients with advanced or metastatic non-small-cell lung cancer: A phase I-II trial","volume":"57","author":"Stathopoulos","year":"2007","journal-title":"Lung Cancer"},{"key":"2025011303332121900_ref46","doi-asserted-by":"publisher","first-page":"219","DOI":"10.21037\/jtd.2017.12.30","article-title":"A pooled analysis of advanced nonsquamous non-small cell lung cancer patients with stable treated brain metastases in two phase II trials receiving bevacizumab and pemetrexed as second-line therapy","volume":"10","author":"Gubens","year":"2018","journal-title":"J Thorac Dis"},{"key":"2025011303332121900_ref47","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1007\/s00280-012-1910-2","article-title":"Phase II study of carboplatin and pemetrexed in advanced non-squamous, non-small-cell lung cancer: Kyoto thoracic oncology research group trial 0902","volume":"70","author":"Kim","year":"2012","journal-title":"Cancer Chemother Pharmacol"},{"key":"2025011303332121900_ref48","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.cllc.2012.10.001","article-title":"A randomized phase II study of pemetrexed in combination with cisplatin or carboplatin as first-line therapy for patients with locally advanced or metastatic non\u2013small-cell lung cancer","volume":"14","author":"Schuette","year":"2013","journal-title":"Clin Lung Cancer"},{"key":"2025011303332121900_ref49","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.lungcan.2013.01.008","article-title":"Pemetrexed and carboplatin, an active option in first-line treatment of elderly patients with advanced non-small cell lung cancer (NSCLC): A phase II trial","volume":"80","author":"Gervais","year":"2013","journal-title":"Lung Cancer"},{"key":"2025011303332121900_ref50","doi-asserted-by":"publisher","first-page":"1718","DOI":"10.1158\/1535-7163.MCT-07-0010","article-title":"5-azacytidine, a DNA methyltransferase inhibitor, induces ATR-mediated DNA double-strand break responses, apoptosis, and synergistic cytotoxicity with doxorubicin and bortezomib against multiple myeloma cells","volume":"6","author":"Kiziltepe","year":"2007","journal-title":"Mol Cancer Ther"},{"key":"2025011303332121900_ref51","doi-asserted-by":"publisher","first-page":"e95765","DOI":"10.1371\/journal.pone.0095765","article-title":"Down-regulation of CD9 by methylation decreased bortezomib sensitivity in multiple myeloma","volume":"9","author":"Hu","year":"2014","journal-title":"PloS One"},{"key":"2025011303332121900_ref52","doi-asserted-by":"publisher","first-page":"giac087","DOI":"10.1093\/gigascience\/giac087","article-title":"SYNPRED: Prediction of drug combination effects in cancer using different synergy metrics and ensemble learning","volume":"11","author":"Preto","year":"2022","journal-title":"GigaScience"},{"key":"2025011303332121900_ref53","doi-asserted-by":"publisher","first-page":"btad390","DOI":"10.1093\/bioinformatics\/btad390","article-title":"Interpretable deep learning architectures for improving drug response prediction performance: Myth or reality?","volume":"39","author":"Li","year":"2023","journal-title":"Bioinformatics"},{"key":"2025011303332121900_ref54","doi-asserted-by":"publisher","first-page":"3128","DOI":"10.1038\/s41598-021-82612-7","article-title":"Explainable drug sensitivity prediction through cancer pathway enrichment","volume":"11","author":"Tang","year":"2021","journal-title":"Sci Rep"},{"key":"2025011303332121900_ref55","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1186\/s12916-022-02549-0","article-title":"NeRD: A multichannel neural network to predict cellular response of drugs by integrating multidimensional data","volume":"20","author":"Cheng","year":"2022","journal-title":"BMC Med"},{"key":"2025011303332121900_ref56","doi-asserted-by":"publisher","first-page":"bbac302","DOI":"10.1093\/bib\/bbac302","article-title":"DTSyn: A dual-transformer-based neural network to predict synergistic drug combinations","volume":"23","author":"Hu","year":"2022","journal-title":"Brief Bioinform"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/1\/bbae717\/61416185\/bbae717.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/1\/bbae717\/61416185\/bbae717.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T03:33:44Z","timestamp":1736739224000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbae717\/7952008"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,22]]},"references-count":56,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,11,22]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbae717","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,1]]},"published":{"date-parts":[[2024,11,22]]},"article-number":"bbae717"}}