{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T15:19:29Z","timestamp":1788016769780,"version":"build-2784847793"},"reference-count":75,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T00:00:00Z","timestamp":1770163200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T00:00:00Z","timestamp":1772755200000},"content-version":"vor","delay-in-days":30,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 62302341"],"award-info":[{"award-number":["No. 62302341"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 62473268"],"award-info":[{"award-number":["No. 62473268"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No.62425107"],"award-info":[{"award-number":["No.62425107"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Genome Biol"],"DOI":"10.1186\/s13059-026-03972-9","type":"journal-article","created":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T05:45:38Z","timestamp":1770183938000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["UniSyn: a multi-modal framework with knowledge transfer for anti-cancer drug synergy prediction"],"prefix":"10.1186","volume":"27","author":[{"given":"Yaojia","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yumeng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengting","family":"Niu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiacheng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhonghao","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quan","family":"Zou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiangning","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ximei","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,4]]},"reference":[{"issue":"3","key":"3972_CR1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1006658","volume":"15","author":"R Nussinov","year":"2019","unstructured":"Nussinov R, Jang H, Tsai CJ, Cheng F. Precision medicine and driver mutations: computational methods, functional assays and conformational principles for interpreting cancer drivers. PLoS Comput Biol. 2019;15(3):e1006658.","journal-title":"PLoS Comput Biol"},{"issue":"565","key":"3972_CR2","doi-asserted-by":"publisher","DOI":"10.1126\/scisignal.aaw3483","volume":"12","author":"MB Yaffe","year":"2019","unstructured":"Yaffe MB. Why geneticists stole cancer research even though cancer is primarily a signaling disease. Sci Signal. 2019;12(565):eaaw3483.","journal-title":"Sci Signal"},{"issue":"5","key":"3972_CR3","doi-asserted-by":"publisher","first-page":"646","DOI":"10.1016\/j.cell.2011.02.013","volume":"144","author":"D Hanahan","year":"2011","unstructured":"Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646\u201374.","journal-title":"Cell"},{"issue":"4","key":"3972_CR4","doi-asserted-by":"publisher","first-page":"725","DOI":"10.1093\/carcin\/bgt086","volume":"34","author":"K Masui","year":"2013","unstructured":"Masui K, Gini B, Wykosky J, Zanca C, Mischel PS, Furnari FB, et al. A tale of two approaches: complementary mechanisms of cytotoxic and targeted therapy resistance may inform next-generation cancer treatments. Carcinogenesis. 2013;34(4):725\u201338.","journal-title":"Carcinogenesis"},{"issue":"7","key":"3972_CR5","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1038\/nbt.1549","volume":"27","author":"J Leh\u00e1r","year":"2009","unstructured":"Leh\u00e1r J, Krueger AS, Avery W, Heilbut AM, Johansen LM, Price ER, et al. Synergistic drug combinations tend to improve therapeutically relevant selectivity. Nat Biotechnol. 2009;27(7):659\u201366.","journal-title":"Nat Biotechnol"},{"issue":"3","key":"3972_CR6","doi-asserted-by":"publisher","DOI":"10.1002\/prp2.149","volume":"3","author":"J Foucquier","year":"2015","unstructured":"Foucquier J, Guedj M. Analysis of drug combinations: current methodological landscape. Pharmacol Res Perspect. 2015;3(3):e00149.","journal-title":"Pharmacol Res Perspect"},{"issue":"3","key":"3972_CR7","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1038\/s41579-018-0141-x","volume":"17","author":"M Tyers","year":"2019","unstructured":"Tyers M, Wright GD. Drug combinations: a strategy to extend the life of antibiotics in the 21st century. Nat Rev Microbiol. 2019;17(3):141\u201355.","journal-title":"Nat Rev Microbiol"},{"issue":"6474","key":"3972_CR8","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1126\/science.aay3041","volume":"367","author":"J Liu","year":"2020","unstructured":"Liu J, Gefen O, Ronin I, Bar-Meir M, Balaban NQ. Effect of tolerance on the evolution of antibiotic resistance under drug combinations. Science. 2020;367(6474):200\u20134.","journal-title":"Science"},{"issue":"7","key":"3972_CR9","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1038\/nbt.2284","volume":"30","author":"B Al-Lazikani","year":"2012","unstructured":"Al-Lazikani B, Banerji U, Workman P. Combinatorial drug therapy for cancer in the post-genomic era. Nat Biotechnol. 2012;30(7):679\u201392.","journal-title":"Nat Biotechnol"},{"issue":"1","key":"3972_CR10","doi-asserted-by":"publisher","first-page":"3197","DOI":"10.1080\/10717544.2022.2132018","volume":"29","author":"MK Iqubal","year":"2022","unstructured":"Iqubal MK, Kaur H, Md S, Alhakamy NA, Iqubal A, Ali J, et al. A technical note on emerging combination approach involved in the onconanotherapeutics. Drug Deliv. 2022;29(1):3197\u2013212.","journal-title":"Drug Deliv"},{"issue":"1","key":"3972_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-025-59634-0","volume":"16","author":"J Qiao","year":"2025","unstructured":"Qiao J, Jin J, Wang D, Teng S, Zhang J, Yang X, et al. A self-conformation-aware pre-training framework for molecular property prediction with substructure interpretability. Nat Commun. 2025;16(1):1\u201316.","journal-title":"Nat Commun"},{"issue":"5","key":"3972_CR12","doi-asserted-by":"publisher","first-page":"744","DOI":"10.1038\/s41591-019-0407-5","volume":"25","author":"JK Sicklick","year":"2019","unstructured":"Sicklick JK, Kato S, Okamura R, Schwaederle M, Hahn ME, Williams CB, et al. Molecular profiling of cancer patients enables personalized combination therapy: the I-PREDICT study. Nat Med. 2019;25(5):744\u201350.","journal-title":"Nat Med"},{"issue":"1","key":"3972_CR13","doi-asserted-by":"publisher","first-page":"3","DOI":"10.2174\/2211738507666190122111224","volume":"7","author":"FA Fisusi","year":"2019","unstructured":"Fisusi FA, Akala EO. Drug combinations in breast cancer therapy. Pharm Nanotechnol. 2019;7(1):3\u201323.","journal-title":"Pharm Nanotechnol"},{"issue":"13","key":"3972_CR14","doi-asserted-by":"publisher","first-page":"3564","DOI":"10.1158\/0008-5472.CAN-17-0489","volume":"77","author":"SL Holbeck","year":"2017","unstructured":"Holbeck SL, Camalier R, Crowell JA, Govindharajulu JP, Hollingshead M, Anderson LW, et al. The National Cancer Institute ALMANAC: a comprehensive screening resource for the detection of anticancer drug pairs with enhanced therapeutic activity. Cancer Res. 2017;77(13):3564\u201376.","journal-title":"Cancer Res"},{"issue":"6","key":"3972_CR15","doi-asserted-by":"publisher","DOI":"10.15252\/msb.202211517","volume":"19","author":"M Lotfollahi","year":"2023","unstructured":"Lotfollahi M, Klimovskaia Susmelj A, De Donno C, Hetzel L, Ji Y, Ibarra IL, et al. Predicting cellular responses to complex perturbations in high-throughput screens. Mol Syst Biol. 2023;19(6):e11517.","journal-title":"Mol Syst Biol"},{"key":"3972_CR16","doi-asserted-by":"crossref","unstructured":"He L, Kulesskiy E, Saarela J, Turunen L, Wennerberg K, Aittokallio T, et al. Methods for high-throughput drug combination screening and synergy scoring. In: von Stechow L, editor. Cancer systems biology: methods and protocols. New York, NY: Springer New York; 2018. p. 351\u201398.","DOI":"10.1007\/978-1-4939-7493-1_17"},{"issue":"1","key":"3972_CR17","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.pharmthera.2012.01.001","volume":"134","author":"A Astashkina","year":"2012","unstructured":"Astashkina A, Mann B, Grainger DW. A critical evaluation of in vitro cell culture models for high-throughput drug screening and toxicity. Pharmacol Ther. 2012;134(1):82\u2013106.","journal-title":"Pharmacol Ther"},{"issue":"6","key":"3972_CR18","doi-asserted-by":"publisher","first-page":"1155","DOI":"10.1158\/1535-7163.MCT-15-0843","volume":"15","author":"J O'Neil","year":"2016","unstructured":"O\u2019Neil J, Benita Y, Feldman I, Chenard M, Roberts B, Liu Y, et al. An unbiased oncology compound screen to identify novel combination strategies. Mol Cancer Ther. 2016;15(6):1155\u201362.","journal-title":"Mol Cancer Ther"},{"issue":"1","key":"3972_CR19","doi-asserted-by":"publisher","DOI":"10.1186\/s13073-016-0369-x","volume":"8","author":"D Day","year":"2016","unstructured":"Day D, Siu LL. Approaches to modernize the combination drug development paradigm. Genome Med. 2016;8(1):115.","journal-title":"Genome Med"},{"issue":"1","key":"3972_CR20","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-19950-z","volume":"11","author":"H Julkunen","year":"2020","unstructured":"Julkunen H, Cichonska A, Gautam P, Szedmak S, Douat J, Pahikkala T, et al. Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects. Nat Commun. 2020;11(1):6136.","journal-title":"Nat Commun"},{"issue":"1","key":"3972_CR21","doi-asserted-by":"publisher","first-page":"5848","DOI":"10.1038\/s41467-020-19563-6","volume":"11","author":"A Ling","year":"2020","unstructured":"Ling A, Huang RS. Computationally predicting clinical drug combination efficacy with cancer cell line screens and independent drug action. Nat Commun. 2020;11(1):5848.","journal-title":"Nat Commun"},{"key":"3972_CR22","doi-asserted-by":"crossref","unstructured":"Mahapatra M, Sahu C, Mohapatra S. Trends of Artificial Intelligence (AI) use in drug targets, discovery and development: current status and future perspectives. Current drug targets. 2024:22.","DOI":"10.2174\/0113894501322734241008163304"},{"issue":"6","key":"3972_CR23","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1012229","volume":"20","author":"C Ai","year":"2024","unstructured":"Ai C, Yang H, Liu X, Dong R, Ding Y, Guo F. MTMol-GPT: de novo multi-target molecular generation with transformer-based generative adversarial imitation learning. PLoS Comput Biol. 2024;20(6):e1012229.","journal-title":"PLoS Comput Biol"},{"issue":"1","key":"3972_CR24","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab355","volume":"23","author":"L Wu","year":"2022","unstructured":"Wu L, Wen Y, Leng D, Zhang Q, Dai C, Wang Z, et al. Machine learning methods, databases and tools for drug combination prediction. Brief Bioinform. 2022;23(1):bbab355.","journal-title":"Brief Bioinform"},{"key":"3972_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102894","volume":"117","author":"Y Qian","year":"2025","unstructured":"Qian Y, Wang Y, Liu J, Zou Q, Ding Y, Guo X, et al. A survey on multi-view fusion for predicting links in biomedical bipartite networks: methods and applications. Inf Fusion. 2025;117:102894.","journal-title":"Inf Fusion"},{"issue":"1","key":"3972_CR26","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-41698-5","volume":"14","author":"Y Wang","year":"2023","unstructured":"Wang Y, Pang C, Wang Y, Jin J, Zhang J, Zeng X, et al. Retrosynthesis prediction with an interpretable deep-learning framework based on molecular assembly tasks. Nat Commun. 2023;14(1):6155.","journal-title":"Nat Commun"},{"issue":"6","key":"3972_CR27","doi-asserted-by":"publisher","first-page":"e1011214","DOI":"10.1371\/journal.pcbi.1011214","volume":"19","author":"H Li","year":"2023","unstructured":"Li H, Liu B. BioSeq-Diabolo: biological sequence similarity analysis using Diabolo. PLoS Comput Biol. 2023;19(6):e1011214.","journal-title":"PLoS Comput Biol"},{"key":"3972_CR28","doi-asserted-by":"publisher","first-page":"2807","DOI":"10.1016\/j.csbj.2022.05.055","volume":"20","author":"W Kong","year":"2022","unstructured":"Kong W, Midena G, Chen Y, Athanasiadis P, Wang T, Rousu J, et al. Systematic review of computational methods for drug combination prediction. Comput Struct Biotechnol J. 2022;20:2807\u201314.","journal-title":"Comput Struct Biotechnol J"},{"issue":"11","key":"3972_CR29","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-024-4171-9","volume":"67","author":"Y Wang","year":"2024","unstructured":"Wang Y, Zhai Y, Ding Y, Zou Q. SBSM-Pro: support bio-sequence machine for proteins. Sci China Inf Sci. 2024;67(11):212106.","journal-title":"Sci China Inf Sci"},{"issue":"5","key":"3972_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijantimicag.2024.107122","volume":"63","author":"J Qin","year":"2024","unstructured":"Qin J, Yang Y, Ai C, Ji Z, Chen W, Song Y, et al. Antibiotic combinations prediction based on machine learning to multicentre clinical data and drug interaction correlation. Int J Antimicrob Agents. 2024;63(5):107122.","journal-title":"Int J Antimicrob Agents"},{"key":"3972_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-024-4461-0","author":"R Qi","year":"2025","unstructured":"Qi R, Liu S, Hui X, Shaytan AK, Liu B. AI in drug development: advances in response, combination therapy, repositioning and molecular design. Sci China Inf Sci. 2025. https:\/\/doi.org\/10.1007\/s11432-024-4461-0.","journal-title":"Sci China Inf Sci"},{"issue":"4","key":"3972_CR32","doi-asserted-by":"publisher","first-page":"2334","DOI":"10.1109\/TCBB.2021.3086702","volume":"19","author":"HI Kuru","year":"2022","unstructured":"Kuru HI, Tastan O, Cicek AE. Matchmaker: a deep learning framework for drug synergy prediction. IEEE ACM Trans Comput Biol Bioinform. 2022;19(4):2334\u201344.","journal-title":"IEEE ACM Trans Comput Biol Bioinform"},{"issue":"1","key":"3972_CR33","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-023-00690-3","volume":"15","author":"M Xu","year":"2023","unstructured":"Xu M, Zhao X, Wang J, Feng W, Wen N, Wang C, et al. DFFNDDS: prediction of synergistic drug combinations with dual feature fusion networks. J Cheminform. 2023;15(1):33.","journal-title":"J Cheminform"},{"issue":"1","key":"3972_CR34","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-024-00839-8","volume":"16","author":"X Zhao","year":"2024","unstructured":"Zhao X, Xu J, Shui Y, Xu M, Hu J, Liu X, et al. PermuteDDS: a permutable feature fusion network for drug-drug synergy prediction. J Cheminform. 2024;16(1):41.","journal-title":"J Cheminform"},{"issue":"10","key":"3972_CR35","doi-asserted-by":"publisher","first-page":"btae604","DOI":"10.1093\/bioinformatics\/btae604","volume":"40","author":"X Li","year":"2024","unstructured":"Li X, Shen B, Feng F, Li K, Tang Z, Ma L, et al. Dual-view jointly learning improves personalized drug synergy prediction. Bioinformatics. 2024;40(10):btae604.","journal-title":"Bioinformatics"},{"issue":"20","key":"3972_CR36","doi-asserted-by":"publisher","first-page":"4782","DOI":"10.1093\/bioinformatics\/btac579","volume":"38","author":"X Liu","year":"2022","unstructured":"Liu X, Song C, Liu S, Li M, Zhou X, Zhang W. Multi-way relation-enhanced hypergraph representation learning for anti-cancer drug synergy prediction. Bioinformatics. 2022;38(20):4782\u20139.","journal-title":"Bioinformatics"},{"issue":"5","key":"3972_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11704-024-40072-y","volume":"19","author":"Z Huang","year":"2025","unstructured":"Huang Z, Xiao Z, Ao C, Guan L, Yu L. Computational approaches for predicting drug-disease associations: a comprehensive review. Front Comput Sci. 2025;19(5):1\u201315.","journal-title":"Front Comput Sci"},{"issue":"1","key":"3972_CR38","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1186\/s12915-024-02085-8","volume":"22","author":"Z Huang","year":"2024","unstructured":"Huang Z, Guo X, Qin J, Gao L, Ju F, Zhao C, et al. Accurate RNA velocity estimation based on multibatch network reveals complex lineage in batch scRNA-seq data. BMC Biol. 2024;22(1):290.","journal-title":"BMC Biol"},{"issue":"1","key":"3972_CR39","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1186\/s12915-023-01796-8","volume":"21","author":"H Zhu","year":"2023","unstructured":"Zhu H, Hao H, Yu L. Identifying disease-related microbes based on multi-scale variational graph autoencoder embedding Wasserstein distance. BMC Biol. 2023;21(1):294.","journal-title":"BMC Biol"},{"issue":"Supplement_2","key":"3972_CR40","doi-asserted-by":"publisher","first-page":"i911","DOI":"10.1093\/bioinformatics\/btaa822","volume":"36","author":"Q Liu","year":"2020","unstructured":"Liu Q, Hu Z, Jiang R, Zhou M. DeepCDR: a hybrid graph convolutional network for predicting cancer drug response. Bioinformatics. 2020;36(Supplement_2):i911\u20138.","journal-title":"Bioinformatics"},{"issue":"5600","key":"3972_CR41","doi-asserted-by":"publisher","first-page":"1912","DOI":"10.1126\/science.1075762","volume":"298","author":"G Manning","year":"2002","unstructured":"Manning G, Whyte DB, Martinez R, Hunter T, Sudarsanam S. The protein kinase complement of the human genome. Science. 2002;298(5600):1912\u201334.","journal-title":"Science"},{"issue":"1","key":"3972_CR42","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1038\/s42003-023-04783-5","volume":"6","author":"H Zhang","year":"2023","unstructured":"Zhang H, Wang Z, Nan Y, Zagidullin B, Yi D, Tang J, et al. Harmonizing across datasets to improve the transferability of drug combination prediction. Commun Biol. 2023;6(1):397.","journal-title":"Commun Biol"},{"issue":"7391","key":"3972_CR43","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1038\/nature11003","volume":"483","author":"J Barretina","year":"2012","unstructured":"Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, et al. The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483(7391):603\u20137.","journal-title":"Nature"},{"issue":"7757","key":"3972_CR44","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1038\/s41586-019-1186-3","volume":"569","author":"M Ghandi","year":"2019","unstructured":"Ghandi M, Huang FW, Jan\u00e9-Valbuena J, Kryukov GV, Lo CC, McDonald ER, et al. Next-generation characterization of the cancer cell line encyclopedia. Nature. 2019;569(7757):503\u20138.","journal-title":"Nature"},{"issue":"4","key":"3972_CR45","doi-asserted-by":"publisher","first-page":"2170","DOI":"10.1182\/blood-2007-07-100362","volume":"111","author":"S Boehrer","year":"2008","unstructured":"Boehrer S, Ad\u00e8s L, Braun T, Galluzzi L, Grosjean J, Fabre C, et al. Erlotinib exhibits antineoplastic off-target effects in AML and MDS: a preclinical study. Blood. 2008;111(4):2170\u201380.","journal-title":"Blood"},{"key":"3972_CR46","doi-asserted-by":"publisher","first-page":"v178","DOI":"10.1093\/annonc\/mdz244.035","volume":"30","author":"SCFA Huijberts","year":"2019","unstructured":"Huijberts SCFA, van Brummelen E, van Geel R, Opdam FL, Marchetti S, Steeghs N, et al. 473P - Phase I study of lapatinib and trametinib in patients with KRAS mutant colorectal, non-small cell lung and pancreatic cancer. Ann Oncol. 2019;30:v178\u20139.","journal-title":"Ann Oncol"},{"issue":"6","key":"3972_CR47","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1038\/nrmicro2133","volume":"7","author":"PJ Yeh","year":"2009","unstructured":"Yeh PJ, Hegreness MJ, Aiden AP, Kishony R. Drug interactions and the evolution of antibiotic resistance. Nat Rev Microbiol. 2009;7(6):460\u20136.","journal-title":"Nat Rev Microbiol"},{"issue":"2","key":"3972_CR48","doi-asserted-by":"publisher","DOI":"10.1038\/s41419-017-0132-2","volume":"9","author":"D Chen","year":"2018","unstructured":"Chen D, Lin X, Zhang C, Liu Z, Chen Z, Li Z, et al. Dual PI3K\/mTOR inhibitor BEZ235 as a promising therapeutic strategy against paclitaxel-resistant gastric cancer via targeting PI3K\/Akt\/mTOR pathway. Cell Death Dis. 2018;9(2):123.","journal-title":"Cell Death Dis"},{"issue":"4","key":"3972_CR49","doi-asserted-by":"publisher","first-page":"161","DOI":"10.3892\/etm.2024.12449","volume":"27","author":"Y Su","year":"2024","unstructured":"Su Y, Wu M, Zhou B, Bai Z, Pang R, Liu Z, et al. Paclitaxel mediates the PI3K\/AKT\/mTOR pathway to reduce proliferation of FLT3-ITD+ AML cells and promote apoptosis. Exp Ther Med. 2024;27(4):161.","journal-title":"Exp Ther Med"},{"issue":"7","key":"3972_CR50","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.1038\/mt.2014.27","volume":"22","author":"E Blanco","year":"2014","unstructured":"Blanco E, Sangai T, Wu S, Hsiao A, Ruiz-Esparza GU, Gonzalez-Delgado CA, et al. Colocalized delivery of Rapamycin and Paclitaxel to tumors enhances synergistic targeting of the PI3K\/Akt\/mTOR pathway. Mol Ther. 2014;22(7):1310\u20139.","journal-title":"Mol Ther"},{"issue":"5","key":"3972_CR51","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.1016\/j.cell.2013.08.003","volume":"154","author":"A Basu","year":"2013","unstructured":"Basu A, Bodycombe Nicole E, Cheah Jaime H, Price Edmund V, Liu K, Schaefer Giannina I, et al. An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules. Cell. 2013;154(5):1151\u201361.","journal-title":"Cell"},{"issue":"2","key":"3972_CR52","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/j.cell.2021.12.038","volume":"185","author":"JA Collora","year":"2022","unstructured":"Collora JA, Ho Y-C. The loud minority: transcriptionally active HIV-1-infected cells survive, proliferate, and persist. Cell. 2022;185(2):227\u20139.","journal-title":"Cell"},{"issue":"2","key":"3972_CR53","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.cell.2018.03.035","volume":"173","author":"F Sanchez-Vega","year":"2018","unstructured":"Sanchez-Vega F, Mina M, Armenia J, Chatila WK, Luna A, La KC, et al. Oncogenic signaling pathways in the cancer genome atlas. Cell. 2018;173(2):321-37.e10.","journal-title":"Cell"},{"issue":"5","key":"3972_CR54","doi-asserted-by":"publisher","first-page":"672","DOI":"10.1016\/j.ccell.2020.09.014","volume":"38","author":"BM Kuenzi","year":"2020","unstructured":"Kuenzi BM, Park J, Fong SH, Sanchez KS, Lee J, Kreisberg JF, et al. Predicting drug response and synergy using a deep learning model of human cancer cells. Cancer Cell. 2020;38(5):672-84.e6.","journal-title":"Cancer Cell"},{"issue":"W1","key":"3972_CR55","doi-asserted-by":"publisher","first-page":"W43","DOI":"10.1093\/nar\/gkz337","volume":"47","author":"B Zagidullin","year":"2019","unstructured":"Zagidullin B, Aldahdooh J, Zheng S, Wang W, Wang Y, Saad J, et al. Drugcomb: an integrative cancer drug combination data portal. Nucleic Acids Res. 2019;47(W1):W43\u201351.","journal-title":"Nucleic Acids Res"},{"issue":"W1","key":"3972_CR56","doi-asserted-by":"publisher","first-page":"W174","DOI":"10.1093\/nar\/gkab438","volume":"49","author":"S Zheng","year":"2021","unstructured":"Zheng S, Aldahdooh J, Shadbahr T, Wang Y, Aldahdooh D, Bao J, et al. Drugcomb update: a more comprehensive drug sensitivity data repository and analysis portal. Nucleic Acids Res. 2021;49(W1):W174\u201384.","journal-title":"Nucleic Acids Res"},{"issue":"5","key":"3972_CR57","doi-asserted-by":"publisher","first-page":"e1006752","DOI":"10.1371\/journal.pcbi.1006752","volume":"15","author":"A Malyutina","year":"2019","unstructured":"Malyutina A, Majumder MM, Wang W, Pessia A, Heckman CA, Tang J. Drug combination sensitivity scoring facilitates the discovery of synergistic and efficacious drug combinations in cancer. PLoS Comput Biol. 2019;15(5):e1006752.","journal-title":"PLoS Comput Biol"},{"issue":"3","key":"3972_CR58","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1111\/j.1744-7348.1939.tb06990.x","volume":"26","author":"CI Bliss","year":"1939","unstructured":"Bliss CI. The toxicity of poisons applied jointly. Ann Appl Biol. 1939;26(3):585\u2013615.","journal-title":"Ann Appl Biol"},{"issue":"13","key":"3972_CR59","doi-asserted-by":"publisher","first-page":"7977","DOI":"10.1073\/pnas.1337088100","volume":"100","author":"AA Borisy","year":"2003","unstructured":"Borisy AA, Elliott PJ, Hurst NW, Lee MS, Leh\u00e1r J, Price ER, et al. Systematic discovery of multicomponent therapeutics. Proc Natl Acad Sci U S A. 2003;100(13):7977\u201382.","journal-title":"Proc Natl Acad Sci U S A"},{"key":"3972_CR60","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1016\/j.csbj.2015.09.001","volume":"13","author":"B Yadav","year":"2015","unstructured":"Yadav B, Wennerberg K, Aittokallio T, Tang J. Searching for drug synergy in complex dose-response landscapes using an interaction potency model. Comput Struct Biotechnol J. 2015;13:504\u201313.","journal-title":"Comput Struct Biotechnol J"},{"key":"3972_CR61","unstructured":"Loewe S. The problem of synergism and antagonism of combined drugs. Arzneimittel-forschung. 1953;3(6):285\u201390."},{"issue":"6","key":"3972_CR62","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1016\/j.cels.2017.05.002","volume":"4","author":"GC Forcina","year":"2017","unstructured":"Forcina GC, Conlon M, Wells A, Cao JY, Dixon SJ. Systematic quantification of population cell death kinetics in mammalian cells. Cell Syst. 2017;4(6):600-10.e6.","journal-title":"Cell Syst"},{"issue":"7","key":"3972_CR63","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1038\/nchembio.2382","volume":"13","author":"MP Licciardello","year":"2017","unstructured":"Licciardello MP, Ringler A, Markt P, Klepsch F, Lardeau C-H, Sdelci S, et al. A combinatorial screen of the CLOUD uncovers a synergy targeting the androgen receptor. Nat Chem Biol. 2017;13(7):771\u20138.","journal-title":"Nat Chem Biol"},{"issue":"5","key":"3972_CR64","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1021\/ci100050t","volume":"50","author":"D Rogers","year":"2010","unstructured":"Rogers D, Hahn M. Extended-connectivity fingerprints. J Chem Inf Model. 2010;50(5):742\u201354.","journal-title":"J Chem Inf Model"},{"key":"3972_CR65","unstructured":"Landrum G. RDKit: a software suite for cheminformatics, computational chemistry, and predictive modeling. https:\/\/www.rdkit.org\/RDKit_Overview.pdf."},{"key":"3972_CR66","unstructured":"Chithrananda S, Grand G, Ramsundar BJae-p. ChemBERTa: large-scale self-supervised pretraining for molecular property prediction. arXiv preprint. 2020."},{"key":"3972_CR67","doi-asserted-by":"crossref","unstructured":"Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH. PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res. 2009;37(suppl_2):W623-W33.","DOI":"10.1093\/nar\/gkp456"},{"key":"3972_CR68","unstructured":"Xu K, Hu W, Leskovec J, Jegelka S. How powerful are graph neural networks? International Conference on Learning Representations (ICLR); New Orleans, Louisiana, United States. 2019."},{"key":"3972_CR69","unstructured":"Xu K, Li C, Tian Y, Sonobe T, Kawarabayashi KI, Jegelka S. Representation learning on graphs with jumping knowledge networks. In: Jennifer D, Andreas K, editors. Proceedings of the 35th International Conference on Machine Learning; Proceedings of Machine Learning Research: PMLR; 2018. p. 5453--62."},{"issue":"12","key":"3972_CR70","doi-asserted-by":"publisher","first-page":"4797","DOI":"10.1021\/acs.molpharmaceut.9b00520","volume":"16","author":"M Manica","year":"2019","unstructured":"Manica M, Oskooei A, Born J, Subramanian V, S\u00e1ez-Rodr\u00edguez J, Rodr\u00edguez Mart\u00ednez M. Toward explainable anticancer compound sensitivity prediction via multimodal attention-based convolutional encoders. Mol Pharm. 2019;16(12):4797\u2013806.","journal-title":"Mol Pharm"},{"key":"3972_CR71","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need. Proceedings of the 31st International Conference on Neural Information Processing Systems; Long Beach, California, USA: Curran Associates Inc.; 2017. p. 6000\u201310."},{"issue":"4","key":"3972_CR72","first-page":"4318","volume":"36","author":"C Yang","year":"2022","unstructured":"Yang C, Pan J, Gao X, Jiang T, Liu D, Chen G. Cross-task knowledge distillation in multi-task recommendation. Proc AAAI Conf Artif Intell. 2022;36(4):4318\u201326.","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"D1","key":"3972_CR73","doi-asserted-by":"publisher","first-page":"D457","DOI":"10.1093\/nar\/gkv1070","volume":"44","author":"M Kanehisa","year":"2016","unstructured":"Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M. Kegg as a reference resource for gene and protein annotation. Nucleic Acids Res. 2016;44(D1):D457\u201362.","journal-title":"Nucleic Acids Res"},{"key":"3972_CR74","unstructured":"The Gene Ontology C. The Gene Ontology resource: enriching a GOld mine. Nucleic Acids Res. 2021;49(D1):D325-D34."},{"key":"3972_CR75","doi-asserted-by":"publisher","unstructured":"Chen Y. codejiajia\/UniSyn: v1.0.0 (v1.0.0). Zenodo. 2025. https:\/\/doi.org\/10.5281\/zenodo.17313540.","DOI":"10.5281\/zenodo.17313540"}],"container-title":["Genome Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13059-026-03972-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13059-026-03972-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13059-026-03972-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T12:04:16Z","timestamp":1772798656000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s13059-026-03972-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,4]]},"references-count":75,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["3972"],"URL":"https:\/\/doi.org\/10.1186\/s13059-026-03972-9","relation":{},"ISSN":["1474-760X"],"issn-type":[{"value":"1474-760X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,4]]},"assertion":[{"value":"21 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"74"}}