DrugBank 5.0: a major update to the DrugBank database for 2018
DrugBank 5.0: a major update to the DrugBank database for 2018 is a dataset published in Nucleic Acids Research (2017). On theSindex it has a DataRank of 14.6, placing it in the top 13.6% of the data-sharing corpus. It has been cited 8,733 times, with 196 citing works in its 1-hop citation network. Its calibrated FAIR score is 60/100.
Abstract
DrugBank (www.drugbank.ca) is a web-enabled database containing comprehensive molecular information about drugs, their mechanisms, their interactions and their targets. First described in 2006, DrugBank has continued to evolve over the past 12 years in response to marked improvements to web standards and changing needs for drug research and development. This year's update, DrugBank 5.0, represents the most significant upgrade to the database in more than 10 years. In many cases, existing data content has grown by 100% or more over the last update. For instance, the total number of investigational drugs in the database has grown by almost 300%, the number of drug-drug interactions has grown by nearly 600% and the number of SNP-associated drug effects has grown more than 3000%. Significant improvements have been made to the quantity, quality and consistency of drug indications, drug binding data as well as drug-drug and drug-food interactions. A great deal of brand new data have also been added to DrugBank 5.0. This includes information on the influence of hundreds of drugs on metabolite levels (pharmacometabolomics), gene expression levels (pharmacotranscriptomics) and protein expression levels (pharmacoprotoemics). New data have also been added on the status of hundreds of new drug clinical trials and existing drug repurposing trials. Many other important improvements in the content, interface and performance of the DrugBank website have been made and these should greatly enhance its ease of use, utility and potential applications in many areas of pharmacological research, pharmaceutical science and drug education.
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Indexed in repositories
- Open Access
- DataCite relations
- Linked datasets
- Dataset classification
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
DataRank Breakdown
Base Score Contribution
1.4
From this paper's citation signal
Citation Network Contribution
13.2
From 196 citing papers with measurable signal
Top 5 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- Metascape provides a biologist-oriented resource for the analysis of systems-level datasetsNature Communications201915,474 citationsDataRank 14.7Top 13%
- HMDB 5.0: the Human Metabolome Database for 2022Nucleic Acids Research20212,377 citationsDataRank 8.8Top 23%
- Constructing knowledge graphs and their biomedical applicationsComputational and Structural Biotechnology Journal2020266 citationsDataRank 0.838
- Evaluating drug targets through human loss-of-function genetic variationNature2020179 citationsDataRank 0.779
- Machine learning for perturbational single-cell omicsCell Systems2021114 citationsDataRank 0.712
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 9% comes from its base citations and 91% from the citation network (196 citing papers contributed measurable signal).
- Base score B(p)
- log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
- Network N(p)
- Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
- Damping factor d = 0.85
- DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
- Self-citations excluded
- Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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