ArticleNucleic acids research2019
DrugComb: an integrative cancer drug combination data portal.
Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 142 papers.
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Who cites it
142 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
- Mechanism-Informed Language Modeling and Oxygenated 3D Screening Identify Berberine-Enzalutamide Synergy in Prostate Cancer Models.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A Machine Learning Approach for Predicting Synergism of Mixtures of Compounds in Treating Cancers.International journal of molecular sciences · 2026Article
- Article
- Virtual cells: progress and challenges from mechanistic modeling to data-driven integration.Briefings in bioinformatics · 2026Review
- How Advanced Artificial Intelligence Technologies Shape Drug-Drug and Drug-Target Interaction Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- DORSSAA: Drug-Target interactOmics Resource Based on Stability/Solubility Alteration Assay.Molecular & cellular proteomics : MCP · 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
- A dual granular balanced deep forest model for effective drug combination prediction.iScience · 2026Article
- Interpretable graph deep learning framework for drug synergy prediction by integrating functional and clinical similarities.NPJ digital medicine · 2026Article
- Learning drug synergy through environment-conditioned feature modulation.Bioinformatics (Oxford, England) · 2026Article
- A causal inference framework for identifying essential genes to enhance drug synergy prediction.Bioinformatics (Oxford, England) · 2026Article
- TensoGraph: a tensor-transformer framework for global-local drug synergy prediction on heterogeneous graphs.Briefings in bioinformatics · 2026Article
- UniSyn: a multi-modal framework with knowledge transfer for anti-cancer drug synergy prediction.Genome biology · 2026Article
- Machine learning models for drug-drug interaction prediction from computational discovery to clinical application.NPJ digital medicine · 2026Review
- PAIRWISE: Deep Learning-based Prediction of Effective Personalized Drug Combinations in Cancer.Research square · 2026Article
- Multiomics Profiling of T-cell Leukemia and Lymphoma Enables Targeted Therapeutic Discovery.Cancer research · 2026Article
- Characterizing clinical toxicity in cancer combination therapies.Bioinformatics (Oxford, England) · 2026Article
- PredictRx: AI based decision support tool for molecular screening for breast cancer drug recommendation.Frontiers in artificial intelligence · 2026Article
- MetaComb: a meta-learning framework for drug combination response prediction from cell lines to patients.Frontiers in genetics · 2026Article
82 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of drug resistance. However, discovery of synergistic and effective drug combinations has been a laborious and often serendipitous process. In recent years, identification of combination therapies has been accelerated due to the advances in high-throughput drug screening, but informatics approaches for systems-level data management and analysis are needed. To contribute toward this goal, we created an open-access data portal called DrugComb (https://drugcomb.fimm.fi) where the results of drug combination screening studies are accumulated, standardized and harmonized. Through the data portal, we provided a web server to analyze and visualize users' own drug combination screening data. The users can also effectively participate a crowdsourcing data curation effect by depositing their data at DrugComb. To initiate the data repository, we collected 437 932 drug combinations tested on a variety of cancer cell lines. We showed that linear regression approaches, when considering chemical fingerprints as predictors, have the potential to achieve high accuracy of predicting the sensitivity of drug combinations. All the data and informatics tools are freely available in DrugComb to enable a more efficient utilization of data resources for future drug combination discovery.
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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.