DFFNDDS: prediction of synergistic drug combinations with dual feature fusion networks
Abstract Drug combination therapies are promising clinical treatments for curing patients. However, efficiently identifying valid drug combinations remains challenging because the number of available drugs has increased rapidly. In this study, we proposed a deep learning model called the Dual Featur...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-03-01
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| Series: | Journal of Cheminformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13321-023-00690-3 |
| _version_ | 1797863716698456064 |
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| author | Mengdie Xu Xinwei Zhao Jingyu Wang Wei Feng Naifeng Wen Chunyu Wang Junjie Wang Yun Liu Lingling Zhao |
| author_facet | Mengdie Xu Xinwei Zhao Jingyu Wang Wei Feng Naifeng Wen Chunyu Wang Junjie Wang Yun Liu Lingling Zhao |
| author_sort | Mengdie Xu |
| collection | DOAJ |
| description | Abstract Drug combination therapies are promising clinical treatments for curing patients. However, efficiently identifying valid drug combinations remains challenging because the number of available drugs has increased rapidly. In this study, we proposed a deep learning model called the Dual Feature Fusion Network for Drug–Drug Synergy prediction (DFFNDDS) that utilizes a fine-tuned pretrained language model and dual feature fusion mechanism to predict synergistic drug combinations. The dual feature fusion mechanism fuses the drug features and cell line features at the bit-wise level and the vector-wise level. We demonstrated that DFFNDDS outperforms competitive methods and can serve as a reliable tool for identifying synergistic drug combinations. |
| first_indexed | 2024-04-09T22:40:47Z |
| format | Article |
| id | doaj.art-a9e513e8ffaa487dad14dde48a5f1407 |
| institution | Directory Open Access Journal |
| issn | 1758-2946 |
| language | English |
| last_indexed | 2024-04-09T22:40:47Z |
| publishDate | 2023-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Cheminformatics |
| spelling | doaj.art-a9e513e8ffaa487dad14dde48a5f14072023-03-22T12:13:30ZengBMCJournal of Cheminformatics1758-29462023-03-0115111210.1186/s13321-023-00690-3DFFNDDS: prediction of synergistic drug combinations with dual feature fusion networksMengdie Xu0Xinwei Zhao1Jingyu Wang2Wei Feng3Naifeng Wen4Chunyu Wang5Junjie Wang6Yun Liu7Lingling Zhao8Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical UniversityDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical UniversityDepartment of Epidemiology, School of Public Health, Nanjing Medical UniversityDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical UniversitySchool of Mechanical and Electrical Engineering, Dalian Minzu UniversityFaculty of Computing, Harbin Institute of TechnologyDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical UniversityDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical UniversityFaculty of Computing, Harbin Institute of TechnologyAbstract Drug combination therapies are promising clinical treatments for curing patients. However, efficiently identifying valid drug combinations remains challenging because the number of available drugs has increased rapidly. In this study, we proposed a deep learning model called the Dual Feature Fusion Network for Drug–Drug Synergy prediction (DFFNDDS) that utilizes a fine-tuned pretrained language model and dual feature fusion mechanism to predict synergistic drug combinations. The dual feature fusion mechanism fuses the drug features and cell line features at the bit-wise level and the vector-wise level. We demonstrated that DFFNDDS outperforms competitive methods and can serve as a reliable tool for identifying synergistic drug combinations.https://doi.org/10.1186/s13321-023-00690-3Drug combinationSynergistic effectDeep learningDual-feature fusion |
| spellingShingle | Mengdie Xu Xinwei Zhao Jingyu Wang Wei Feng Naifeng Wen Chunyu Wang Junjie Wang Yun Liu Lingling Zhao DFFNDDS: prediction of synergistic drug combinations with dual feature fusion networks Journal of Cheminformatics Drug combination Synergistic effect Deep learning Dual-feature fusion |
| title | DFFNDDS: prediction of synergistic drug combinations with dual feature fusion networks |
| title_full | DFFNDDS: prediction of synergistic drug combinations with dual feature fusion networks |
| title_fullStr | DFFNDDS: prediction of synergistic drug combinations with dual feature fusion networks |
| title_full_unstemmed | DFFNDDS: prediction of synergistic drug combinations with dual feature fusion networks |
| title_short | DFFNDDS: prediction of synergistic drug combinations with dual feature fusion networks |
| title_sort | dffndds prediction of synergistic drug combinations with dual feature fusion networks |
| topic | Drug combination Synergistic effect Deep learning Dual-feature fusion |
| url | https://doi.org/10.1186/s13321-023-00690-3 |
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