ArticleNucleic acids research2020
DrugCombDB: a comprehensive database of drug combinations toward the discovery of combinatorial therapy.
Article in Nucleic acids research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 123 papers.
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123 citing papers in PubMed.
- GATRSyn: Advancing Anticancer Drug Synergy Prediction Through Graph Attention Networks and Transformer-based Feature Re-embedding.Interdisciplinary sciences, computational life sciences · 2026Article
- MGCLSyn: multimodal graph contrastive learning for drug synergy prediction.Journal of computer-aided molecular design · 2026Article
- Closing the Translational Gap: Closed-Loop AI Discovery Frameworks for Experimental Validation and Clinical Implementation in Cancer Therapeutics.Cancer medicine · 2026Review
- EpiSNPdb: A Comprehensive Database of Genetic Epistasis Across Multiple Cancer Types.Current issues in molecular biology · 2026Article
- Unified heterogeneity-aware benchmark of drug synergy prediction: a cross-study analysis of traditional machine learning and graph deep learning models.Journal of cheminformatics · 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
- Metab8D: a metabolic regulome network from multiomics and machine learning.Communications biology · 2026Article
- Mechanism-based prediction of drug synergy via network controllability analysis of therapeutic pathways in intractable diseases.iScience · 2026Article
- LaMGen: LLM-based 3D molecular generation for multi-target drug design.Nature communications · 2026Article
- A scalable multimodal graph neural network for drug combination response prediction.Molecular diversity · 2026Article
- A shape-constrained regression and wild bootstrap framework for reproducible drug synergy testing.bioRxiv : the preprint server for biology · 2026Article
- TensoGraph: a tensor-transformer framework for global-local drug synergy prediction on heterogeneous graphs.Briefings in bioinformatics · 2026Article
- Machine learning models for drug-drug interaction prediction from computational discovery to clinical application.NPJ digital medicine · 2026Review
- Progress on Omega-3 fatty acids for the comprehensive and targeted treatment of spinal cord injury.Bone research · 2026Review
- Leveraging network pharmacology in the treatment of asthma.ADMET & DMPK · 2026Review
- A directed weighted network-based method for drug combinations identification using drug-target and inter-target regulation.BMC bioinformatics · 2025Article
- KGLGANSynergy: knowledge graph-based local and global attention network for drug synergy prediction.Journal of translational medicine · 2025Article
- A review of deep learning approaches for drug synergy prediction in cancer.npj drug discovery · 2025Review
- Few-shot drug synergy prediction via rapid cross-tier adaptation meta-optimization.Briefings in bioinformatics · 2025Article
- SynVerse: a modular framework for building and evaluating deep learning-based drug synergy prediction models.Briefings in bioinformatics · 2025Article
63 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug combinations have demonstrated high efficacy and low adverse side effects compared to single drug administration in cancer therapies and thus have drawn intensive attention from researchers and pharmaceutical enterprises. Due to the rapid development of high-throughput screening (HTS), the number of drug combination datasets available has increased tremendously in recent years. Therefore, there is an urgent need for a comprehensive database that is crucial to both experimental and computational screening of synergistic drug combinations. In this paper, we present DrugCombDB, a comprehensive database devoted to the curation of drug combinations from various data sources: (i) HTS assays of drug combinations; (ii) manual curations from the literature; and (iii) FDA Orange Book and external databases. Specifically, DrugCombDB includes 448 555 drug combinations derived from HTS assays, covering 2887 unique drugs and 124 human cancer cell lines. In particular, DrugCombDB has more than 6000 000 quantitative dose responses from which we computed multiple synergy scores to determine the overall synergistic or antagonistic effects of drug combinations. In addition to the combinations extracted from existing databases, we manually curated 457 drug combinations from thousands of PubMed publications. To benefit the further experimental validation and development of computational models, multiple datasets that are ready to train prediction models for classification and regression analysis were constructed and other significant related data were gathered. A website with a user-friendly graphical visualization has been developed for users to access the wealth of data and download prebuilt datasets. Our database is available at http://drugcombdb.denglab.org/.
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