Computational frameworks transform antagonism to synergy in optimizing combination therapies
Abstract While drug combinations are increasingly important in disease treatment, predicting their therapeutic interactions remains challenging. This review systematically analyzes computational methods for predicting drug combination effects through multi-omics data integration. We comprehensively...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01435-2 |
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| _version_ | 1832594440152154112 |
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| author | Jinghong Chen Anqi Lin Aimin Jiang Chang Qi Zaoqu Liu Quan Cheng Shuofeng Yuan Peng Luo |
| author_facet | Jinghong Chen Anqi Lin Aimin Jiang Chang Qi Zaoqu Liu Quan Cheng Shuofeng Yuan Peng Luo |
| author_sort | Jinghong Chen |
| collection | DOAJ |
| description | Abstract While drug combinations are increasingly important in disease treatment, predicting their therapeutic interactions remains challenging. This review systematically analyzes computational methods for predicting drug combination effects through multi-omics data integration. We comprehensively assess key algorithms including DrugComboRanker and AuDNNsynergy, and evaluate integration approaches encompassing kernel regression and graph networks. The review elucidates artificial intelligence applications in predicting drug synergistic and antagonistic effects. |
| format | Article |
| id | doaj-art-cb208adee4514c2cab6642c0f3a41184 |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-cb208adee4514c2cab6642c0f3a411842025-01-19T12:39:55ZengNature Portfolionpj Digital Medicine2398-63522025-01-018112210.1038/s41746-025-01435-2Computational frameworks transform antagonism to synergy in optimizing combination therapiesJinghong Chen0Anqi Lin1Aimin Jiang2Chang Qi3Zaoqu Liu4Quan Cheng5Shuofeng Yuan6Peng Luo7Department of Oncology, Zhujiang Hospital, Southern Medical UniversityDepartment of Oncology, Zhujiang Hospital, Southern Medical UniversityDepartment of Urology, Changhai Hospital, Naval Medical University (Second Military Medical University)Vienna University of Technology, Institute of Logic and ComputationInstitute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical CollegeDepartment of Neurosurgery, Xiangya Hospital Central South UniversityDepartment of Infectious Disease and Microbiology, The University of Hong Kong-Shenzhen HospitalDepartment of Oncology, Zhujiang Hospital, Southern Medical UniversityAbstract While drug combinations are increasingly important in disease treatment, predicting their therapeutic interactions remains challenging. This review systematically analyzes computational methods for predicting drug combination effects through multi-omics data integration. We comprehensively assess key algorithms including DrugComboRanker and AuDNNsynergy, and evaluate integration approaches encompassing kernel regression and graph networks. The review elucidates artificial intelligence applications in predicting drug synergistic and antagonistic effects.https://doi.org/10.1038/s41746-025-01435-2 |
| spellingShingle | Jinghong Chen Anqi Lin Aimin Jiang Chang Qi Zaoqu Liu Quan Cheng Shuofeng Yuan Peng Luo Computational frameworks transform antagonism to synergy in optimizing combination therapies npj Digital Medicine |
| title | Computational frameworks transform antagonism to synergy in optimizing combination therapies |
| title_full | Computational frameworks transform antagonism to synergy in optimizing combination therapies |
| title_fullStr | Computational frameworks transform antagonism to synergy in optimizing combination therapies |
| title_full_unstemmed | Computational frameworks transform antagonism to synergy in optimizing combination therapies |
| title_short | Computational frameworks transform antagonism to synergy in optimizing combination therapies |
| title_sort | computational frameworks transform antagonism to synergy in optimizing combination therapies |
| url | https://doi.org/10.1038/s41746-025-01435-2 |
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