ReviewBriefings in bioinformatics2018
Predictive approaches for drug combination discovery in cancer.
Review in Briefings in bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 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
58 citing papers in PubMed, 122 citations in OpenAlex.
- Targeting protein protein interactions and their modulators to enable new therapeutic strategies for human diseases.NPJ systems biology and applications · 2026Review
- MetaComb: a meta-learning framework for drug combination response prediction from cell lines to patients.Frontiers in genetics · 2026Article
- SynergyGraph: predicting cell line specific drug combination synergy scores using knowledge graph representation and hypergraph modeling.Scientific reports · 2025Article
- A review of deep learning approaches for drug synergy prediction in cancer.npj drug discovery · 2025Review
- Lipid-based nanocarriers in combination chemotherapy: a promising strategy for advanced skin cancer management.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- Innovative horizons: harnessing drug repositioning for targeted therapeutics in colorectal cancer.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- Environment-by-Environment Interactions Differ Across Genetic Backgrounds.Molecular biology and evolution · 2025Article
- Multi-modal contrastive drug synergy prediction model guided by single modality.Journal of cheminformatics · 2025Article
- A computational dynamic model of combination treatment for type II inhibitors with asciminib.Protein science : a publication of the Protein Society · 2025Article
- Sensitive detection of synthetic response to cancer immunotherapy driven by gene paralog pairs.Patterns (New York, N.Y.) · 2025Article
- HDN-DDI: a novel framework for predicting drug-drug interactions using hierarchical molecular graphs and enhanced dual-view representation learning.BMC bioinformatics · 2025Article
- Computational frameworks transform antagonism to synergy in optimizing combination therapies.NPJ digital medicine · 2025Review
- PolyLLM: polypharmacy side effect prediction via LLM-based SMILES encodings.Frontiers in pharmacology · 2025Article
- Predicting effective drug combinations for cancer treatment using a graph-based approach.Synthetic and systems biotechnology · 2025Article
- Anticancer drug synergy prediction based on CatBoost.PeerJ. Computer science · 2025Article
- Network-based estimation of therapeutic efficacy and adverse reaction potential for prioritisation of anti-cancer drug combinations.Computational and structural biotechnology journal · 2025Article
- Review of Predicting Synergistic Drug Combinations.Life (Basel, Switzerland) · 2023Review
- A target map of clinical combination therapies in oncology: an analysis of clinicaltrials.gov.Discover oncology · 2023Article
- DeepTraSynergy: drug combinations using multimodal deep learning with transformers.Bioinformatics (Oxford, England) · 2023Article
- Identification of the Stapled α-Helical Peptide ATSP-7041 as a Substrate and Strong Inhibitor of OATP1B1 In Vitro.Biomolecules · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Drug combinations have been proposed as a promising therapeutic strategy to overcome drug resistance and improve efficacy of monotherapy regimens in cancer. This strategy aims at targeting multiple components of this complex disease. Despite the increasing number of drug combinations in use, many of them were empirically found in the clinic, and the molecular mechanisms underlying these drug combinations are often unclear. These challenges call for rational, systematic approaches for drug combination discovery. Although high-throughput screening of single-agent therapeutics has been successfully implemented, it is not feasible to test all possible drug combinations, even for a reduced subset of anticancer drugs. Hence, in vitro and in vivo screening of a large number of drug combinations are not practical. Therefore, devising computational methods to efficiently explore the space of drug combinations and to discover efficacious combinations has attracted a lot of attention from the scientific community in the past few years. Nevertheless, in the absence of consensus regarding the computational approaches used to predict efficacious drug combinations, a plethora of methods, techniques and hypotheses have been developed to date, while the research field lacks an elaborate categorization of the existing computational methods and the available data sources. In this manuscript, we review and categorize the state-of-the-art computational approaches for drug combination prediction, and elaborate on the limitations of these methods and the existing challenges. We also discuss about the recent pan-cancer drug combination data sets and their importance in revising the available methods or developing more performant approaches.
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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.