ArticleBriefings in bioinformatics2024
SynergyX: a multi-modality mutual attention network for interpretable drug synergy prediction.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- MGCLSyn: multimodal graph contrastive learning for drug synergy prediction.Journal of computer-aided molecular design · 2026Article
- GATESynergy: Integrating Molecular Global-Local Aggregator and Hierarchical Gene-Gated Encoder for Drug Synergy Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- CLC-Pred Synergy: Web Application for Predicting Pairwise Drug Combinations with Synergistic Activity Against NCI60 Cancer Cell Lines.International journal of molecular sciences · 2026Article
- A causal inference framework for identifying essential genes to enhance drug synergy prediction.Bioinformatics (Oxford, England) · 2026Article
- A systematic study of parameter sharing strategies in multi-task learning for drug synergy and sensitivity prediction.Journal of computer-aided molecular design · 2026Article
- A scalable multimodal graph neural network for drug combination response prediction.Molecular diversity · 2026Article
- DeepDrugs: a mechanism-aware tri-linear attention framework for synergistic drug-combination prediction.Briefings in bioinformatics · 2026Article
- Cosynllm: predicting drug combination synergy with LLM-generated descriptions.Journal of cheminformatics · 2026Article
- GATRSyn: Advancing Anticancer Drug Synergy Prediction Through Graph Attention Networks and Transformer-based Feature Re-embedding.Interdisciplinary sciences, computational life sciences · 2026Article
- Research progress of artificial intelligence in high-throughput drug screening.Frontiers in pharmacology · 2026Review
- A review of deep learning approaches for drug synergy prediction in cancer.npj drug discovery · 2025Review
- BridgeSyn: a bridging fusion framework for drug combination synergy prediction.Briefings in bioinformatics · 2025Article
- SenolyticSynergy: An Attention-Based Network for Discovering Novel Senolytic Combinations via Human Aging Genomics.International journal of molecular sciences · 2025Article
- Machine Learning for Multi-Target Drug Discovery: Challenges and Opportunities in Systems Pharmacology.Pharmaceutics · 2025Review
- PanThera: predictive analysis of higher-order combination therapies using deep neural networks.Briefings in bioinformatics · 2025Article
- PathSynergy: a deep learning model for predicting drug synergy in liver cancer.Briefings in bioinformatics · 2025Article
- Elucidating the role of artificial intelligence in drug development from the perspective of drug-target interactions.Journal of pharmaceutical analysis · 2025Review
- Optimizing Model Performance and Interpretability: Application to Biological Data Classification.Genes · 2025Article
- CDFA: Calibrated deep feature aggregation for screening synergistic drug combinations.Frontiers in pharmacology · 2025Article
- Improving synergistic drug combination prediction with signature-based gene expression features in oncology.Frontiers in pharmacology · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Discovering effective anti-tumor drug combinations is crucial for advancing cancer therapy. Taking full account of intricate biological interactions is highly important in accurately predicting drug synergy. However, the extremely limited prior knowledge poses great challenges in developing current computational methods. To address this, we introduce SynergyX, a multi-modality mutual attention network to improve anti-tumor drug synergy prediction. It dynamically captures cross-modal interactions, allowing for the modeling of complex biological networks and drug interactions. A convolution-augmented attention structure is adopted to integrate multi-omic data in this framework effectively. Compared with other state-of-the-art models, SynergyX demonstrates superior predictive accuracy in both the General Test and Blind Test and cross-dataset validation. By exhaustively screening combinations of approved drugs, SynergyX reveals its ability to identify promising drug combination candidates for potential lung cancer treatment. Another notable advantage lies in its multidimensional interpretability. Taking Sorafenib and Vorinostat as an example, SynergyX serves as a powerful tool for uncovering drug-gene interactions and deciphering cell selectivity mechanisms. In summary, SynergyX provides an illuminating and interpretable framework, poised to catalyze the expedition of drug synergy discovery and deepen our comprehension of rational combination therapy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.