ArticleScientific data2018
A large-scale dataset of in vivo pharmacology assay results.
Article in Scientific data, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Integrating artificial intelligence and manual curation to enhance bioassay annotations in ChEMBL.Journal of cheminformatics · 2026Article
- NPASS database update 2026: comprehensive quantitative composition, bioactivity, and ADME-Tox data of natural products for biomedical research.Nucleic acids research · 2026Article
- Fifteen years of ChEMBL and its role in cheminformatics and drug discovery.Journal of cheminformatics · 2025Review
- The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods.Nucleic acids research · 2024Article
- Development of In Silico Methods for Toxicity Prediction in Collaboration Between Academia and the Pharmaceutical Industry.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Precision medicine review: rare driver mutations and their biophysical classification.Biophysical reviews · 2019Review
- Evolving applications of the egg: chorioallantoic membrane assay andJournal of tissue engineeringReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
ChEMBL is a large-scale, open-access drug discovery resource containing bioactivity information primarily extracted from scientific literature. A substantial dataset of more than 135,000 in vivo assays has been collated as a key resource of animal models for translational medicine within drug discovery. To improve the utility of the in vivo data, an extensive data curation task has been undertaken that allows the assays to be grouped by animal disease model or phenotypic endpoint. The dataset contains previously unavailable information about compounds or drugs tested in animal models and, in conjunction with assay data on protein targets or cell- or tissue- based systems, allows the investigation of the effects of compounds at differing levels of biological complexity. Equally, it enables researchers to identify compounds that have been investigated for a group of disease-, pharmacology- or toxicity-relevant assays.
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.