SynCell: Contextualized Drug Synergy Prediction
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914414084489216 |
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| author | Peng, Keqin Su, Guangxin Shi, Qinshan Gao, Shuai Wang, Ren Chen, Can Wen, Jun |
| author_facet | Peng, Keqin Su, Guangxin Shi, Qinshan Gao, Shuai Wang, Ren Chen, Can Wen, Jun |
| contents | Drug synergy is profoundly influenced by cellular context, as variations in protein interaction landscapes and pathway activities across cell types reshape how drugs act in combination. Most existing models overlook this heterogeneity, relying on static or bulk-level protein-protein interaction (PPI) networks that ignore cell-specific molecular wiring. The availability of large-scale transcriptomic data now enables the reconstruction of cell-line-resolved interactomes, offering a new foundation for contextualized drug synergy modeling.
Here we present SynCell, a Contextualized Drug Synergy framework that integrates drug-protein, protein-protein, and protein-cell line relations within a unified graph architecture. SynCell leverages cell-line-specific PPI networks to embed the molecular context in which drugs act, and employs graph convolutional learning to model how pharmacological effects propagate through cell-specific signaling networks. This formulation treats synergy prediction as a cell-line-contextualized drug-drug interaction problem. Across the large-scale DrugCombDB benchmark, SynCell consistently outperforms state-of-the-art baselines - including DeepSynergy, HypergraphSynergy, HERMES, BAITSAO, DTF, and NHP - particularly in predicting synergies involving unseen drugs or novel cell lines. When benchmarked against these seven methods, SynCell demonstrates substantial gains in generalization and biological interpretability, confirming that contextualizing PPIs with cell-line resolution is indispensable for accurate synergy prediction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_17695 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SynCell: Contextualized Drug Synergy Prediction Peng, Keqin Su, Guangxin Shi, Qinshan Gao, Shuai Wang, Ren Chen, Can Wen, Jun Quantitative Methods Drug synergy is profoundly influenced by cellular context, as variations in protein interaction landscapes and pathway activities across cell types reshape how drugs act in combination. Most existing models overlook this heterogeneity, relying on static or bulk-level protein-protein interaction (PPI) networks that ignore cell-specific molecular wiring. The availability of large-scale transcriptomic data now enables the reconstruction of cell-line-resolved interactomes, offering a new foundation for contextualized drug synergy modeling. Here we present SynCell, a Contextualized Drug Synergy framework that integrates drug-protein, protein-protein, and protein-cell line relations within a unified graph architecture. SynCell leverages cell-line-specific PPI networks to embed the molecular context in which drugs act, and employs graph convolutional learning to model how pharmacological effects propagate through cell-specific signaling networks. This formulation treats synergy prediction as a cell-line-contextualized drug-drug interaction problem. Across the large-scale DrugCombDB benchmark, SynCell consistently outperforms state-of-the-art baselines - including DeepSynergy, HypergraphSynergy, HERMES, BAITSAO, DTF, and NHP - particularly in predicting synergies involving unseen drugs or novel cell lines. When benchmarked against these seven methods, SynCell demonstrates substantial gains in generalization and biological interpretability, confirming that contextualizing PPIs with cell-line resolution is indispensable for accurate synergy prediction. |
| title | SynCell: Contextualized Drug Synergy Prediction |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2511.17695 |