ArticleNature machine intelligence2019
Prediction of drug combination effects with a minimal set of experiments.
Article in Nature machine intelligence, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 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
90 citing papers in PubMed, 178 citations in OpenAlex.
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- Standardizing antiviral response metrics for mono- and combination therapies in acute, chronic and latent viral infections.Virology journal · 2026Review
- Combining an anti-HER2 antibody-drug conjugate with an anti-nectin-4 antibody-drug conjugate enhances efficacy in breast cancer and gastric cancer models.Cancer cell international · 2026Article
- DeepDrugs: a mechanism-aware tri-linear attention framework for synergistic drug-combination prediction.Briefings in bioinformatics · 2026Article
- Predictors of response and rational combinations for the novel MCL-1 inhibitor MIK665 in acute myeloid leukemia.Molecular oncology · 2026Article
- PU.1 inhibition sensitizes stem-monocytic AML to BCL2 blockade.bioRxiv : the preprint server for biology · 2026Article
- 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
- Model-informed prediction of antiviral drug combination synergy from sparse data using complementary mechanistic and pharmacodynamic approaches.Frontiers in pharmacology · 2026Article
- MetaComb: a meta-learning framework for drug combination response prediction from cell lines to patients.Frontiers in genetics · 2026Article
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- NovelCancer drug resistance (Alhambra, Calif.) · 2026Article
- Synergistic antiarrhythmic mechanism of IJournal of translational medicine · 2025Article
- An open-source screening platform accelerates discovery of drug combinations.Nature communications · 2025Article
- Proteomics discovery of MTDH and SND1 interaction vulnerabilities in ovarian cancer.Scientific reports · 2025Article
- VCTatDot and VCTatMLP: novel deep learning models with triadic attention embeddings for synergistic drug combination prediction.Scientific reports · 2025Article
- Proteasome inhibition overcomes resistance to targeted therapies in B-cell malignancy models and in an index patient.Cell death & disease · 2025Article
30 more citing papers are in PubMed but not listed here.
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 2 countries.
Funding
Abstract
High-throughput drug combination screening provides a systematic strategy to discover unexpected combinatorial synergies in pre-clinical cell models. However, phenotypic combinatorial screening with multi-dose matrix assays is experimentally expensive, especially when the aim is to identify selective combination synergies across a large panel of cell lines or patient samples. Here we implemented DECREASE, an efficient machine learning model that requires only a limited set of pairwise dose-response measurements for accurate prediction of drug combination synergy and antagonism. Using a compendium of 23,595 drug combination matrices tested in various cancer cell lines, and malaria and Ebola infection models, we demonstrate how cost-effective experimental designs with DECREASE capture almost the same degree of information for synergy and antagonism detection as the fully-measured dose-response matrices. Measuring only the diagonal of the matrix provides an accurate and practical option for combinatorial screening. The open-source web-implementation enables applications of DECREASE to both pre-clinical and translational studies.
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.