Authors
Lei Wang, Liping Sun, Jingwen Pan, Guangming Shao, Guokun Shi, Guanyu Zhang, Yucheng Shi, Li Jin
Published in
PloS one. Volume 21. Issue 11. Pages e0362586. Epub Nov 10, 2026.
Abstract
Experimental screening of synergistic anticancer drug pairs is time-consuming, while effectively integrating complementary molecular graph representations with cell-line-specific information remains challenging for computational drug-synergy prediction.
We developed MFMNet with a Tri-Layer Graph Pyramid (TGP) comprising GCN, GIN, and GAT branches for extracting complementary molecular graph representations. A Cross-Scale Attention Router (CSAR), implemented as an adaptive feature-wise gating mechanism, integrates the graph representations before they are combined with drug embeddings and cell-line transcriptomic features for synergistic drug-pair prediction.
In the initial five-fold cross-validation analysis, MFMNet achieved a mean five-fold AUROC of 0.9301 ± 0.0036. Under the stricter fold-matched evaluation protocol, the full MFMNet backbone achieved an AUROC of 0.9227 ± 0.0055 and a PR-AUC of 0.9127 ± 0.0084, with statistically significant improvements over XGBoost, DeepDDS-GCN, and DeepDDS-GAT after Holm correction.
MFMNet provides a computational framework for synergistic drug-pair prediction by integrating complementary molecular graph representations with drug and cell-line features. The results support its use for computational prioritization of candidate synergistic drug combinations. All reported performance estimates refer to record-level five-fold cross-validation within the O'Neil dataset; generalization to entirely unseen drugs and cell lines requires further study.
PMID:
43007996
Bibliographic data and abstract were imported from PubMed on 11 Nov 2026.
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