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QSAR DataBank repository: open and linked qualitative and quantitative structure-activity relationship models
V Ruusmann1, S Sild1, U Maran1
1Institute of Chemistry, University of Tartu, Ravila 14a, 50411 Tartu, Estonia.
Journal of Cheminformatics
|June 26, 2015
Summary
A new digital repository, the QSAR DataBank (QsarDB), makes in silico quantitative structure-activity relationship (QSAR) models easily discoverable, citable, and reusable. This enhances collaboration and decision-making in scientific research.
Area of Science:
- Computational chemistry and cheminformatics
- Drug discovery and development
- Toxicology and environmental science
Background:
- Structure-activity relationship (SAR) models have a long history in scientific research for predictions.
- Quantitative structure-activity relationships [(Q)SARs] are valuable but hindered by poor model accessibility.
- A novel digital repository is presented to address these limitations.
Purpose of the Study:
- To create a digital repository for in silico (Q)SAR models.
- To make these models archivable, citable, and readily usable.
- To enhance transparency, reproducibility, and accessibility of modeling workflows.
Main Methods:
- Development of the QSAR DataBank (QsarDB) repository.
- Representation of models in the QsarDB data format.
- Implementation of a content-aware (smart) repository for organization and services.
Main Results:
- Models are organized hierarchically by metadata.
- The repository provides browsing, downloading, analysis, visualization, and prediction services.
- QsarDB facilitates collaboration and reuse of existing models.
Conclusions:
- The QsarDB repository unlocks the potential of in silico (Q)SAR models.
- A standardized data format enables easy preservation and sharing of model knowledge.
- Increased productivity for model developers and more confident decisions for users are achieved.
Keywords:
DSpaceDigital object identifierDigital repositoryODOSOSPredictive modelQDBQSARQSPRQSTRQsarDBMore Related Videos
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