ArticleNucleic acids research2021
DrugComb update: a more comprehensive drug sensitivity data repository and analysis portal.
Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
60 citing papers in PubMed, 128 citations in OpenAlex.
- Article
- How Advanced Artificial Intelligence Technologies Shape Drug-Drug and Drug-Target Interaction Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- 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
- A causal inference framework for identifying essential genes to enhance drug synergy prediction.Bioinformatics (Oxford, England) · 2026Article
- DeepDrugs: a mechanism-aware tri-linear attention framework for synergistic drug-combination prediction.Briefings in bioinformatics · 2026Article
- UniSyn: a multi-modal framework with knowledge transfer for anti-cancer drug synergy prediction.Genome biology · 2026Article
- GATRSyn: Advancing Anticancer Drug Synergy Prediction Through Graph Attention Networks and Transformer-based Feature Re-embedding.Interdisciplinary sciences, computational life sciences · 2026Article
- Machine learning models for drug-drug interaction prediction from computational discovery to clinical application.NPJ digital medicine · 2026Review
- Multiomics Profiling of T-cell Leukemia and Lymphoma Enables Targeted Therapeutic Discovery.Cancer research · 2026Article
- MetaComb: a meta-learning framework for drug combination response prediction from cell lines to patients.Frontiers in genetics · 2026Article
- Article
- Optimizing kinase and PARP inhibitor combinations through machine learning and in silico approaches for targeted brain cancer therapy.Molecular diversity · 2025Article
- HIG-Syn: a hypergraph and interaction-aware multigranularity network for predicting synergistic drug combinations.Bioinformatics (Oxford, England) · 2025Article
- Article
- Building a unified model for drug synergy analysis powered by large language models.Nature communications · 2025Article
- SensitiveCancerGPT: Leveraging Generative Large Language Model on Structured Omics Data to Optimize Drug Sensitivity Prediction.bioRxiv : the preprint server for biology · 2025Article
- Advancing personalized cancer therapy: Onko_DrugCombScreen-a novel Shiny app for precision drug combination screening.NAR genomics and bioinformatics · 2025Article
- Racial-Ethnic Disparities in Hearing Aid Use: Price Responsiveness Among Older Adults with Hearing Loss.Journal of racial and ethnic health disparities · 2025Article
- Computational frameworks transform antagonism to synergy in optimizing combination therapies.NPJ digital medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
Combinatorial therapies that target multiple pathways have shown great promises for treating complex diseases. DrugComb (https://drugcomb.org/) is a web-based portal for the deposition and analysis of drug combination screening datasets. Since its first release, DrugComb has received continuous updates on the coverage of data resources, as well as on the functionality of the web server to improve the analysis, visualization and interpretation of drug combination screens. Here, we report significant updates of DrugComb, including: (i) manual curation and harmonization of more comprehensive drug combination and monotherapy screening data, not only for cancers but also for other diseases such as malaria and COVID-19; (ii) enhanced algorithms for assessing the sensitivity and synergy of drug combinations; (iii) network modelling tools to visualize the mechanisms of action of drugs or drug combinations for a given cancer sample and (iv) state-of-the-art machine learning models to predict drug combination sensitivity and synergy. These improvements have been provided with more user-friendly graphical interface and faster database infrastructure, which make DrugComb the most comprehensive web-based resources for the study of drug sensitivities for multiple diseases.
Indexed as
Identifiers
What OpenQuestion holds
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.