ArticleScientific data2016
Systematic chemical-genetic and chemical-chemical interaction datasets for prediction of compound synergism.
Article in Scientific data, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
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Who cites it
9 citing papers in PubMed.
- New hit compounds againstAntimicrobial agents and chemotherapy · 2026Article
- Article
- Small molecule inhibitors of fungal Δ(9) fatty acid desaturase as antifungal agents againstFrontiers in cellular and infection microbiology · 2024Article
- CDCDB: A large and continuously updated drug combination database.Scientific data · 2022Article
- onlineFDR: an R package to control the false discovery rate for growing data repositories.Bioinformatics (Oxford, England) · 2019Article
- Leveraging genetic interactions for adverse drug-drug interaction prediction.PLoS computational biology · 2019Article
- Drug combinations: a strategy to extend the life of antibiotics in the 21st century.Nature reviews. Microbiology · 2019Review
- Chemogenomic model identifies synergistic drug combinations robust to the pathogen microenvironment.PLoS computational biology · 2018Article
- MOSAIC: a chemical-genetic interaction data repository and web resource for exploring chemical modes of action.Bioinformatics (Oxford, England) · 2018Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
The network structure of biological systems suggests that effective therapeutic intervention may require combinations of agents that act synergistically. However, a dearth of systematic chemical combination datasets have limited the development of predictive algorithms for chemical synergism. Here, we report two large datasets of linked chemical-genetic and chemical-chemical interactions in the budding yeast Saccharomyces cerevisiae. We screened 5,518 unique compounds against 242 diverse yeast gene deletion strains to generate an extended chemical-genetic matrix (CGM) of 492,126 chemical-gene interaction measurements. This CGM dataset contained 1,434 genotype-specific inhibitors, termed cryptagens. We selected 128 structurally diverse cryptagens and tested all pairwise combinations to generate a benchmark dataset of 8,128 pairwise chemical-chemical interaction tests for synergy prediction, termed the cryptagen matrix (CM). An accompanying database resource called ChemGRID was developed to enable analysis, visualisation and downloads of all data. The CGM and CM datasets will facilitate the benchmarking of computational approaches for synergy prediction, as well as chemical structure-activity relationship models for anti-fungal drug 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.